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What is Supervised Learning

Chapter 39
Encyclopedia of Data Science and Machine Learning
When the machine learns under supervision, it is called supervised learning. It uses a labelled dataset. Labelled dataset means that it contains the answer or solution to each problem dataset. For example, a labelled animal dataset may contain images with labels like elephant, cat, etc. Machine learning model, trained with the labelled dataset can predict the animal whenever a new animal image fed to the model by comparing that image with the labelled dataset.
Published in Chapter:

Machine Learning and Exploratory Data Analysis in Cross-Sell Insurance

Anand Jha (Rustamji Institute of Technology, BSF Academy, Tekanpur, India) and Brajkishore Prajapati (Rustamji Institute of Technology, BSF Academy, Tekanpur, India)
Copyright: © 2023 | Pages: 35
DOI: 10.4018/978-1-7998-9220-5.ch039

Abstract

Data is playing a central role in the insurance industry. The current journey of insurance industry is conquered by data collection to make future decisions since this is the digital era of the insurance industry in its journey of 700+ years. This chapter focuses on exploratory data analysis (EDA) to identify significant and critical factors to develop business strategy as well as to predict customers' responses in cross-sell health insurance. Response is either acceptance or rejection of a health insurance product offered to existing customers, who may or may not hold policies with the company. Exploratory data analysis (EDA) presents data analysis and visualization from various lookouts to characterize data that can help the insurer in strategic decision making.
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Chapter 6
Artificial Intelligence Applied: Six Actual Projects in Big Organizations
A particular form of learning process that takes place under supervision and that affects the training of an artificial neural networks.
Published in Chapter: Artificial Intelligence Applied: Six Actual Projects in Big Organizations; From: Educational and Social Dimensions of Digital Transformation in Organizations
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Chapter 552
Microarrays
Which uses a predisposed knowledge on the data set and based on this classifies the data.
Published in Chapter: Microarrays; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 7
On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification
A learning model employed by a neural network, whereby the network is presented with training data that is appropriately labeled. In supervised learning, the network knows what output it is supposed to give in response to a given input, and thus, the network tries to adjust its parameters to approach the desired output as part of the learning process.
Published in Chapter: On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification; From: Precision Agriculture Technologies for Food Security and Sustainability
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Chapter 3
Promises and Challenges Relating to Machine Learning Techniques to Predict Areas at Risk of Desertification: A State-of-the-Art Review
Machine learning methods using labeled datasets designed to train algorithms into classifying data accurately.
Published in Chapter: Promises and Challenges Relating to Machine Learning Techniques to Predict Areas at Risk of Desertification: A State-of-the-Art Review; From: Handbook of Research on Driving Socioeconomic Development With Big Data
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Chapter 221
Supervised Learning of Fuzzy Logic Systems
A learning method in which there are two distinct phases to the operation. In the first phase each possible solution to a problem is assessed based on the input signal that is propagated through the system producing output respond. The actual respond produced is then compared with a desired response, generating error signals that are then used as a guide to solve the given problems using supervised learning algorithms.
Published in Chapter: Supervised Learning of Fuzzy Logic Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 8
Emerging Technologies to Increase Energy Efficiency and Decrease Indoor Pollution in University Campuses
It is a subcategory of Machine Learning (and Artificial Intelligence). It is characterized by the use of labelled datasets to train algorithms that classify data or predict results accurately.
Published in Chapter: Emerging Technologies to Increase Energy Efficiency and Decrease Indoor Pollution in University Campuses; From: Strategy, Policy, Practice, and Governance for AI in Higher Education Institutions
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Chapter 5
Marketing and Artificial Intelligence: Personalization at Scale
A machine learning technique that involves providing a machine with data that is labeled.
Published in Chapter: Marketing and Artificial Intelligence: Personalization at Scale; From: Handbook of Research on Applied AI for International Business and Marketing Applications
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Chapter 20
Machine Learning
A method of empirical concept learning from labeled data. The task is to build a classification or prediction model that assigns values of target attribute (class labels or values of numeric target) to previously unseen examples.
Published in Chapter: Machine Learning; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 8
Machine Learning and Deep Learning for Big Data Analysis
In the supervised learning paradigm, a model is trained on a labeled dataset to determine the correlation between the input data and the associated output, allowing the model to be used for classification or prediction.
Published in Chapter: Machine Learning and Deep Learning for Big Data Analysis; From: Big Data Analytics Techniques for Market Intelligence
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Chapter 23
Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model: A Novel Approach Using Machine Learning for Assessment of Credit Card Frauds
This is another name for classification since it performs its task with the help of some labeled data which it has obtained in advance to form a prediction model.
Published in Chapter: Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model: A Novel Approach Using Machine Learning for Assessment of Credit Card Frauds; From: Handbook of Research on Natural Computing for Optimization Problems
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Chapter 167
Data Mining and the KDD Process
Learning process of a predictive model from a set of objects, where a supervisor define classes and supply objects of each class. Once the model has been formulated it can be used to predict the class(es) of new objects.
Published in Chapter: Data Mining and the KDD Process; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 8
Using Sentiment Analytics to Understand Learner Experiences in Serious Games
A machine learning model that maps an input to an output based on predefined input-output pairs (training examples). It requires a pre-labelled (with input and output) training dataset.
Published in Chapter: Using Sentiment Analytics to Understand Learner Experiences in Serious Games; From: Next-Generation Applications and Implementations of Gamification Systems
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Chapter 2
Machine Learning in Computer Vision
A technique of a Machine Learning algorithm. It uses known data to get some prediction about unknown data with the statistical model.
Published in Chapter: Machine Learning in Computer Vision; From: Applications of Machine Learning and Artificial Intelligence in Education
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Chapter 7
Machine Learning: A Revolution in Accounting
Supervised learning is a fundamental approach in machine learning. It involves training a model on labeled data, where each input is associated with a known output. This allows the model to learn how to establish a precise relationship between the input data and the expected outputs.
Published in Chapter: Machine Learning: A Revolution in Accounting; From: Artificial Intelligence Approaches to Sustainable Accounting
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Chapter 170
Ensemble Clustering Data Mining and Databases
The problem of supervised learning is formulated as follows: Let X = { x 1 , …, x m } be a set of m objects or entities. Suppose these objects come from k classes and the membership to each object to exactly one class is known in advance. Then such a set is termed a training set T . It has the form T = {( x 1 , l 1 ) …, ( x m , l m )} where l i denotes the label of a class to which i -th object belongs. The aim of supervised learning is to find a mapping f : X ? L , such that f ( x i ) = l i , for all i = 1, …, m . The function f is said to be decision function or decision rule. If the labels l i are not known we say about unsupervised learning. Its task is to discover a structure in the set X .
Published in Chapter: Ensemble Clustering Data Mining and Databases; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 8
Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques
A method in machine learning uses the model that has been trained to analyze the data.
Published in Chapter: Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 3
Machine Learning Applications for Vibration-Based Structural Health Monitoring
Machine learning task of learning a function that maps an input to an output based on sample input-output pairs.
Published in Chapter: Machine Learning Applications for Vibration-Based Structural Health Monitoring; From: Artificial Intelligence and Machine Learning Techniques for Civil Engineering
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Chapter 533
Identification of Wireless Devices From Their Physical Layer Radio-Frequency Fingerprints
The machine learning task of deducing a function from a set of labeled training data.
Published in Chapter: Identification of Wireless Devices From Their Physical Layer Radio-Frequency Fingerprints; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 21
Introduction to Machine Learning as a New Methodological Framework for Performance Assessment
A type of machine learning which uses a labeled dataset, such that the algorithm attempts to match the output labels based on input data.
Published in Chapter: Introduction to Machine Learning as a New Methodological Framework for Performance Assessment; From: Handbook of Research on Advancements in Organizational Data Collection and Measurements: Strategies for Addressing Attitudes, Beliefs, and Behaviors
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Chapter 4
First of All, Understand Data Analytics Context and Changes
A supervised learning algorithm applies a known set of input data and drives a model to produce reasonable predictions for responses to new data. Supervised learning develops predictive models using classification and regression techniques.
Published in Chapter: First of All, Understand Data Analytics Context and Changes; From: Big Data Analytics for Entrepreneurial Success
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Chapter 10
Use of PCA Solution in Hierarchy and Case of Two Classes of Data Sets to Initialize Parameters for Clustering Function: An Estimation Method for Clustering Function in Classification Application
The supervised learning is the common machine learning in which the training samples with credit are used to find the parameters namely weights of a such as hyperplane for the subsequent classification application.
Published in Chapter: Use of PCA Solution in Hierarchy and Case of Two Classes of Data Sets to Initialize Parameters for Clustering Function: An Estimation Method for Clustering Function in Classification Application; From: Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies
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Chapter 1
Artificial Intelligence and Machine Learning Education and Literacy: Teacher Training for Primary and Secondary Education Teachers
Supervised Learning is a machine learning paradigm in which the system processes examples of data belonging to different categories (for example images of cats, dogs, or humans) and identifies similarities and differences among them so as to learn to identify the category of unseen data.
Published in Chapter: Artificial Intelligence and Machine Learning Education and Literacy: Teacher Training for Primary and Secondary Education Teachers; From: Handbook of Research on Integrating ICTs in STEAM Education
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Chapter 5
Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education
A type of machine learning in which classified output data is used to train the machine and produce the correct algorithms. It is much more common than unsupervised learning.
Published in Chapter: Ethical Navigations: Adaptable Frameworks for Responsible AI Use in Higher Education; From: Exploring the Ethical Implications of Generative AI
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Chapter 6
Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success
A supervised learning algorithm applies a known set of input data and drives a model to produce reasonable predictions for responses to new data. Supervised learning develops predictive models using classification and regression techniques.
Published in Chapter: Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success; From: Big Data Analytics for Entrepreneurial Success
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Chapter 21
Towards an Effective Imaging-Based Decision Support System for Skin Cancer
A machine learning algorithm that learns from labeled data included in a training set.
Published in Chapter: Towards an Effective Imaging-Based Decision Support System for Skin Cancer; From: Handbook of Research on Applied Intelligence for Health and Clinical Informatics
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Chapter 11
The Application of Machine Learning for Predicting Global Seismicity
The relationship between inputs and their outputs that allows to make a future prediction, uses labelled data.
Published in Chapter: The Application of Machine Learning for Predicting Global Seismicity; From: Handbook of Research on AI Methods and Applications in Computer Engineering
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Chapter 19
Learning Aided Digital Image Compression Technique for Medical Application
It is a type of learning where the systems follow a pre-determined pattern. It is like learning with the support of a teacher. It is generally observed in MLP type ANN.
Published in Chapter: Learning Aided Digital Image Compression Technique for Medical Application; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 2
Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree
A machine learning method that maps an input to an output based on the input-output pairs of data
Published in Chapter: Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree; From: Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
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Chapter 576
Artificial Neural Networks and Their Applications in Business
A learning strategy in which the desired output, or dependent attribute, is known.
Published in Chapter: Artificial Neural Networks and Their Applications in Business; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 15
Leveraging VR/AR/MR and AI as Innovative Educational Practices for “iGeneration” Students
A task of learning a function that maps input to the output based on example input-output pairs.
Published in Chapter: Leveraging VR/AR/MR and AI as Innovative Educational Practices for “iGeneration” Students; From: Handbook of Research on Equity in Computer Science in P-16 Education
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Chapter 9
The Role of Artificial Intelligence in Cyber Security
A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student.
Published in Chapter: The Role of Artificial Intelligence in Cyber Security; From: Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems
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Chapter 160
Pattern Recognition Methods
It is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object and a desired output value.
Published in Chapter: Pattern Recognition Methods; From: Encyclopedia of Business Analytics and Optimization
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Chapter 1
Developing an Effective Classification Model for Medical Data Analysis
It is a type of machine learning algorithm that uses a known dataset (called the training dataset) to construct a learned model, which makes predictions for unknown datasets (called the testing datasets).
Published in Chapter: Developing an Effective Classification Model for Medical Data Analysis; From: Advanced Classification Techniques for Healthcare Analysis
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Chapter 10
AI Methods for Analyzing Microarray Data
A learning algorithm that is given a training set consisting of feature vectors associated with class labels and whose goal is to learn a classifier that can predict the class labels of future instances.
Published in Chapter: AI Methods for Analyzing Microarray Data; From: Encyclopedia of Artificial Intelligence
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Chapter 14
Machine Learning for Smart Tourism and Retail
The field in machine learning which is concerned on the development of algorithms that learn functions from labelled data.
Published in Chapter: Machine Learning for Smart Tourism and Retail; From: Handbook of Research on Big Data Clustering and Machine Learning
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Chapter 105
Imbalanced Classification for Business Analytics
A machine learning technique of predicting the value of a given function for any input based on labeled training data.
Published in Chapter: Imbalanced Classification for Business Analytics; From: Encyclopedia of Business Analytics and Optimization
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Chapter 17
Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review
It uses labelled datasets to train algorithms to classify data or predict outcomes accurately.
Published in Chapter: Machine Learning and Sentiment Analysis for Analyzing Customer Feedback: A Review; From: Big Data Analytics Techniques for Market Intelligence
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Chapter 180
Recommending Rating Values on Reviews for Designers
One type of a machine learning task which intends to infer a function from labeled training data.
Published in Chapter: Recommending Rating Values on Reviews for Designers; From: Encyclopedia of Business Analytics and Optimization
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Chapter 4
Influence of Intelligent Automation on Industries and Daily Life
Classification of data to train the machines.
Published in Chapter: Influence of Intelligent Automation on Industries and Daily Life; From: Hyperautomation in Business and Society
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Chapter 4
Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT)
The algorithm is trained by certain data (labels or patterns) and the task is to identify the expected output which is manually supervised, hence called Supervised Learning.
Published in Chapter: Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT); From: Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications
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Chapter 3
Natural Language Processing in Online Reviews
It is machine learning algorithm in which the model learns from ample amount of available labeled data to predict the class of unseen instances.
Published in Chapter: Natural Language Processing in Online Reviews; From: Natural Language Processing for Global and Local Business
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Chapter 53
Machine Learning Algorithms in Human Gait Analysis
A sub-category of machine learning that uses labelled data to infer relationships between the input and output.
Published in Chapter: Machine Learning Algorithms in Human Gait Analysis; From: Encyclopedia of Data Science and Machine Learning
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Chapter 11
Leveraging Machine Learning Techniques to Improve Learning and Recommendations Within Dairy Farms: Towards High Milk Yields for Small-Scale Farmers
A subfield of machine learning that builds and trains models that can reliably predict and generalize well to new datasets using labelled data.
Published in Chapter: Leveraging Machine Learning Techniques to Improve Learning and Recommendations Within Dairy Farms: Towards High Milk Yields for Small-Scale Farmers; From: Impact of Disruptive Technologies on the Socio-Economic Development of Emerging Countries
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Chapter 18
Intelligent Slotting for the Warehouse
This type of learning is similar to the learning demonstrated by human beings, i.e., gaining understanding from past experiences to acquire new knowledge in order to improve the ability to perform real-world tasks. However, machine learning learns from data, since computers do not have “experiences”, which are collected in the past and represent past experiences in some real-world applications.
Published in Chapter: Intelligent Slotting for the Warehouse; From: Handbook of Research on Strategic Supply Chain Management in the Retail Industry
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Chapter 5
Role of Artificial Intelligence in Cyber Security: A Useful Overview
Has data which already has a correct answer whereas, in unsupervised learning, the algorithms cluster the data without any prior knowledge. Reinforcement learning uses a penalty system where the algorithm rewards itself for correct classification and gives a penalty for incorrect one.
Published in Chapter: Role of Artificial Intelligence in Cyber Security: A Useful Overview; From: Information Security Practices for the Internet of Things, 5G, and Next-Generation Wireless Networks
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Chapter 162
Use of “Odds” in Bayesian Classifiers
In supervised learning models (at their development stages) are provided with data/ examples on both input (predicator variables) and output (category) labels.
Published in Chapter: Use of “Odds” in Bayesian Classifiers; From: Encyclopedia of Data Science and Machine Learning
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Chapter 2
Automatic Detection of Emotion in Music: Interaction with Emotionally Sensitive Machines
is a machine learning technique to automatically learn by example. A supervised learning algorithm generates a function predicting ouputs based on input observations. The function is generated from the training data. The training data is made of input observations and wanted outputs. Based on these examples the algorithm aims to generalize properly from the input/ouput observations to unobserved cases. We call it regression when the ouput is a continuous value and classification when the ouput is a label. Supervised learning is opposed to unsupervised learning, where the outputs are unknown. In that case, the algorithm aims to find structures in the data. There are many supervised learning algorithms such as Support Vector Machines, Nearest Neighbors, Decision trees, Naïve Bayes or Artificial Neural Network.
Published in Chapter: Automatic Detection of Emotion in Music: Interaction with Emotionally Sensitive Machines; From: Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence
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Chapter 7
Machine Learning Techniques for IoT-Based Indoor Tracking and Localization
In supervised learning, a mathematical and statistical predictive model is constructed using a raw data set that is already tagged with correct labels.
Published in Chapter: Machine Learning Techniques for IoT-Based Indoor Tracking and Localization; From: Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics
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Chapter 6
Machine Learning Approaches to Automated Medical Decision Support Systems
The knowledge is obtained through a training which includes a data set called the training sample which is structured according to the knowledge base supported by human experts as physicians in medical context, and databases. It is assumed that the user knows beforehand the classes and the instances of each class.
Published in Chapter: Machine Learning Approaches to Automated Medical Decision Support Systems; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 4
Concerning the Integration of Machine Learning Content in Mechatronics Curricula
Machine learning approaches often used for regression and classification.
Published in Chapter: Concerning the Integration of Machine Learning Content in Mechatronics Curricula; From: Revolutionizing Education in the Age of AI and Machine Learning
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Chapter 8
Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis: Transport Domain Adaptation of Sentiment Labelled Data Set
It is an algorithm that uses labelled data and analyses the training data and accordingly produces an inferred model, which can be used to classify new data.
Published in Chapter: Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis: Transport Domain Adaptation of Sentiment Labelled Data Set; From: Natural Language Processing for Global and Local Business
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Chapter 11
Amplifying Participant Voices Through Text Mining
A text mining algorithm, such as email spam filtering, that is developed and refined through the use of a training data set.
Published in Chapter: Amplifying Participant Voices Through Text Mining; From: Advancing Educational Research With Emerging Technology
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Chapter 10
Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services
It is a subcategory of Machine Learning (and Artificial Intelligence). It is characterized by the use of labelled datasets to train algorithms that classify data or predict results accurately.
Published in Chapter: Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services; From: Next-Generation Applications and Implementations of Gamification Systems
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Chapter 6
EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild
In this learning, the model needs a labeled data for training. The model knows in advance the answer to the questions it must predict and tries to learn the relationship between input and output.
Published in Chapter: EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild; From: Design and Implementation of Healthcare Biometric Systems
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Chapter 17
Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak
Making predictions for samples that the learning model has not evaluate before by taking a set of labeled samples as training data.
Published in Chapter: Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak; From: Handbook of Research on Interdisciplinary Perspectives on the Threats and Impacts of Pandemics
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Chapter 23
A Meta-Analytical Review of Deep Learning Prediction Models for Big Data
It is an approach of Artificial Intelligence where computer algorithm is trained on input data that has been labeled for a particular output.
Published in Chapter: A Meta-Analytical Review of Deep Learning Prediction Models for Big Data; From: Encyclopedia of Data Science and Machine Learning
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Chapter 1
Introduction to Artificial Intelligence
Is part of machine learning that uses the input data to predict the output patter with the help of conditions set by the programmer.
Published in Chapter: Introduction to Artificial Intelligence; From: Handbook of Research on AI Methods and Applications in Computer Engineering
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Chapter 575
Applications of Artificial Neural Networks in Economics and Finance
It consists in learning from data with a known-in-advance outcome that is predicted based on a set of inputs, referred to as “features”.
Published in Chapter: Applications of Artificial Neural Networks in Economics and Finance; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 8
Artificial Intelligence in the Delivery of Mobile Tourism Services
The use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.
Published in Chapter: Artificial Intelligence in the Delivery of Mobile Tourism Services; From: Mobile Computing and Technology Applications in Tourism and Hospitality
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Chapter 2
Investigation on Deep Learning Approach for Big Data: Applications and Challenges
Supervised learning is the data mining task of inferring a function from labeled training data.
Published in Chapter: Investigation on Deep Learning Approach for Big Data: Applications and Challenges; From: Handbook of Research on Pattern Engineering System Development for Big Data Analytics
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Chapter 14
Analytics of User Behaviors on Twitter Using Machine Learning
One of the types of machine learning algorithm that trains that data based on both input and output.
Published in Chapter: Analytics of User Behaviors on Twitter Using Machine Learning; From: Handbook of Research on Artificial Intelligence Applications in Literary Works and Social Media
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Chapter 1
Active Learning with SVM
The set of learning algorithms in which the samples in the training dataset are all labelled.
Published in Chapter: Active Learning with SVM; From: Encyclopedia of Artificial Intelligence
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Chapter 22
The Evolution of AI and Its Transformative Effects on Computing: A Comparative Analysis
Supervised learning is a type of machine learning where a computer system is trained to make predictions or take actions based on labeled examples provided by humans. In supervised learning, the computer is given a dataset consisting of input data and corresponding output labels or desired outcomes. The goal is for the computer to learn the relationship between the input and output so that it can accurately predict the output for new, unseen inputs.
Published in Chapter: The Evolution of AI and Its Transformative Effects on Computing: A Comparative Analysis; From: Intelligent Engineering Applications and Applied Sciences for Sustainability
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Chapter 1
Generative AI in Higher Education
A machine learning approach where models are trained on a labeled dataset, which means that the data are already tagged with the correct answer.
Published in Chapter: Generative AI in Higher Education; From: The Role of Generative AI in the Communication Classroom
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Chapter 9
Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry
Computers are educated on labelled training data and then used to predict output in supervised learning, a subset of machine learning.
Published in Chapter: Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry; From: Social Customer Relationship Management (Social-CRM) in the Era of Web 4.0
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Chapter 13
A Comprehensive Review on AI Techniques for Healthcare
It is a machine learning algorithm that maps the input to the output based on a function.
Published in Chapter: A Comprehensive Review on AI Techniques for Healthcare; From: Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications
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Chapter 2
Interdisciplinary Application of Machine Learning, Data Science, and Python for Cricket Analytics
A type of machine learning where the algorithm is trained on labeled data, meaning that the desired output is known beforehand, and the algorithm learns to map the input to the output.
Published in Chapter: Interdisciplinary Application of Machine Learning, Data Science, and Python for Cricket Analytics; From: Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
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Chapter 6
The Exploration of Autonomous Vehicles
Happens when a set of predefined images or numbers have labels on them. It maps an input to an output based on example input-output pairs.
Published in Chapter: The Exploration of Autonomous Vehicles; From: Smart Systems Design, Applications, and Challenges
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Chapter 39
Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques
A machine learning task that particularly deals with developing a function/an equation from labeled datapoints which are composed of feature vectors. The training data consist of a set of labeled datapoints. The function estimated from the training dataset, is hence used for predicting labels for the test dataset, which consists of feature values and a set of labels that is unknown to the system at the time of making predictions.
Published in Chapter: Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques; From: Handbook of Research on Natural Computing for Optimization Problems
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Chapter 15
An Overview of Applications of Artificial Intelligence Using Different Techniques, Algorithms, and Tools
Supervised learning is used when it has full knowledge of each instance's actual values or labels. Basically, it uses a training dataset to develop a prediction model by consuming input data and output values.
Published in Chapter: An Overview of Applications of Artificial Intelligence Using Different Techniques, Algorithms, and Tools; From: Latin American Women and Research Contributions to the IT Field
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Chapter 11
A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks
It is the machine learning technique in which the input and output are based on the input-output pairs.
Published in Chapter: A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks; From: Handbook of Research on Library Response to the COVID-19 Pandemic
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Chapter 323
Advancements in Computer Aided Imaging Diagnostics
A method of machine learning which requires human intervention at the starting or during the learning process.
Published in Chapter: Advancements in Computer Aided Imaging Diagnostics; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 6
Data Science Tools Application for Business Processes Modelling in Aviation
Is a method used to enable machines to classify objects, problems, or situations based on related data fed into the machines. Machines are fed with data such as characteristics, patterns, dimensions, color and height of objects, people or situations repetitively until the machines are able to perform accurate classifications. Supervised learning is a popular technology or concept that is applied to real-life scenarios. Supervised learning is used to provide product recommendations, segment customers based on customer data, diagnose disease based on previous symptoms and perform many other tasks.
Published in Chapter: Data Science Tools Application for Business Processes Modelling in Aviation; From: Cases on Modern Computer Systems in Aviation
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Chapter 17
Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya
The ANN processes the inputs and compares its resulting outputs with the target. Errors are then propagated back through the system, causing the network to adjust the weights which controls the network.
Published in Chapter: Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya; From: Handbook of Research on Computational Simulation and Modeling in Engineering
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Chapter 162
Consensus Clustering
The problem of supervised learning is formulated as follows: Let X = { x 1 , …, x m } be a set of m objects or entities. Suppose these objects come from k classes and the membership to each object to exactly one class is known in advance. Then such a set is termed a training set T . It has the form T = {( x 1 , l 1 ) …, ( x m , l m )} where l i denotes the label of a class to which i -th object belongs. The aim of supervised learning is to find a mapping f : X ? L , such that f ( x i ) = l i , for all i = 1, …, m . The function f is said to be decision function or decision rule. If the labels l i are not known we say about unsupervised learning. Its task is to discover a structure in the set X .
Published in Chapter: Consensus Clustering; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 120
Hierarchical Neuro-Fuzzy Systems Part I
A machine learning technique for creating a function from training data, which consist of pairs of input patterns as well as the desired outputs. Therefore, the learning process depends on the existance of a “teacher” that provides, to each input pattern, the real output value. The output of the function can be a continuous value (called regression), or a class label of the input object (called classification)
Published in Chapter: Hierarchical Neuro-Fuzzy Systems Part I; From: Encyclopedia of Artificial Intelligence
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Chapter 19
Artificial Neural Networks for Business Analytics
A learning strategy of developing an ANN in which the desired output, or dependent attribute, is known.
Published in Chapter: Artificial Neural Networks for Business Analytics; From: Encyclopedia of Business Analytics and Optimization
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Chapter 1
A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect
The machine learning task of inferring a function from labeled training data that consist of a set of training examples.
Published in Chapter: A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect; From: Handbook of Research on Human-Computer Interfaces, Developments, and Applications
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Chapter 52
Artificial Neural Networks and Data Science
A learning strategy in which the desired output, or dependent attribute, is known.
Published in Chapter: Artificial Neural Networks and Data Science; From: Encyclopedia of Data Science and Machine Learning
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Chapter 8
Machine Learning in Python: Diabetes Prediction Using Machine Learning
Machine learning is broadly classified into two: supervised learning and unsupervised learning. In supervised learning, the machine learns from examples. Historical or train data is needed which is given as an input to the machine and a classifier model is formed. A supervised algorithm also needs a target value. On the contrary, unsupervised learning algorithms need neither the train data nor the target value.
Published in Chapter: Machine Learning in Python: Diabetes Prediction Using Machine Learning; From: Handbook of Research on Applications and Implementations of Machine Learning Techniques
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Chapter 64
Differential Learning Expert System in Data Management
This is performed with feed forward nets where training patterns are composed of an input vector and an output vector that are associated with the input and output nodes, respectively. An input vector is presented at the inputs together with a set of desired responses, one for each node. A forward pass is done and the errors or discrepancies, between the desired and actual response for each node in the output layer, are found. These are then used to determine weight changes in the net according to the prevailing learning rule.
Published in Chapter: Differential Learning Expert System in Data Management; From: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
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Chapter 1
The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks
Class of machine learning algorithms that rely on the knowledge of an external supervisor in order to learn.
Published in Chapter: The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks; From: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
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Chapter 20
Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture
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Chapter 11
Clubhouse Experience: Sentiment Analysis of an Alternative Platform From the Eyes of Classic Social Media Users
A supervised learning technique uses a known set of input data and known responses to train a model to make credible predictions for new data.
Published in Chapter: Clubhouse Experience: Sentiment Analysis of an Alternative Platform From the Eyes of Classic Social Media Users; From: Data Mining Approaches for Big Data and Sentiment Analysis in Social Media
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Chapter 2
Machine Learning and Optimization Applications for Soft Robotics
The supervised learning technique aims to train an ML model using pre-labeled data.
Published in Chapter: Machine Learning and Optimization Applications for Soft Robotics; From: Design and Control Advances in Robotics
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Chapter 10
AI-Driven Approaches to Reshape Forensic Practices: Automating the Tedious, Augmenting the Astute
Supervised learning is a form of machine learning where the model is trained using data for which the correct label is already known.
Published in Chapter: AI-Driven Approaches to Reshape Forensic Practices: Automating the Tedious, Augmenting the Astute; From: Cases on Forensic and Criminological Science for Criminal Detection and Avoidance
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Chapter 11
Artificial Neural Networks
A method used to train ANNs in which a training sample with known outcomes is used to enable the ANN to learn. The known outcome values are used to calculate an error term between the known or desired output and what is produced by the ANN to let the ANN know how to adjust its connection weights to minimize the relative error.
Published in Chapter: Artificial Neural Networks; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 18
Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques
A machine learning task designed to learn a function that maps an input onto an output based on a set of training examples (training data). Each training example is a pair consisting of a vector of inputs and an output value. A supervised learning algorithm analyzes the training data and infers a mapping function. A simple example of supervised learning is a regression model.
Published in Chapter: Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques; From: Handbook of Research on Big Data Clustering and Machine Learning
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Chapter 13
Plant Disease Classification Using Deep Learning Techniques
It is a type of machine learning in which an algorithm learns from labeled data to make predictions or decisions about new, unseen data. The goal is to learn a mapping function from the input data to the output data.
Published in Chapter: Plant Disease Classification Using Deep Learning Techniques; From: Effective AI, Blockchain, and E-Governance Applications for Knowledge Discovery and Management
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Chapter 58
Understanding Machine Learning Concepts
It is a type of Machine Learning. It is characterized by the use of labeled datasets to train algorithms that classify data or predict results accurately.
Published in Chapter: Understanding Machine Learning Concepts; From: Encyclopedia of Data Science and Machine Learning
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Chapter 3
Opinion Mining in Tourism: A Study on “Cappadocia Home Cooking” Restaurant
Machine learning is one of the methods. The data is taken from systems that operate on the principle of response to the effect and organized in the input-output order.
Published in Chapter: Opinion Mining in Tourism: A Study on “Cappadocia Home Cooking” Restaurant; From: Handbook of Research on Smart Technology Applications in the Tourism Industry
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Chapter 12
An Extensive Text Mining Study for the Turkish Language: Author Recognition, Sentiment Analysis, and Text Classification
It is a machine learning technique. It generates a function to match the inputs to the desired outputs.
Published in Chapter: An Extensive Text Mining Study for the Turkish Language: Author Recognition, Sentiment Analysis, and Text Classification; From: Natural Language Processing for Global and Local Business
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Chapter 44
Class Prediction in Test Sets with Shifted Distributions
type of learning where the objective is to learn a function that associates a desired output (‘label’) to each input pattern. Supervised learning techniques require a training dataset of examples with their respective desired outputs. Supervised learning is traditionally divided into regression (the desired output is a continuous variable) and classification (the desired output is a class label).
Published in Chapter: Class Prediction in Test Sets with Shifted Distributions; From: Encyclopedia of Artificial Intelligence
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Chapter 13
Application of Machine Learning In Forensic Science
In supervised learning, you train the machine using data which is well “labeled.” Supervised learning allows you to collect data or produce a data output from the previous experience.
Published in Chapter: Application of Machine Learning In Forensic Science; From: Critical Concepts, Standards, and Techniques in Cyber Forensics
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