What is Supervised Learning

1.

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. Learn more in: Machine Learning and Exploratory Data Analysis in Cross-Sell Insurance

2.

A particular form of learning process that takes place under supervision and that affects the training of an artificial neural networks. Learn more in: Artificial Intelligence Applied: Six Actual Projects in Big Organizations

3.

Which uses a predisposed knowledge on the data set and based on this classifies the data. Learn more in: Microarrays

4.

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. Learn more in: On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification

5.

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. Learn more in: Supervised Learning of Fuzzy Logic Systems

6.

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. Learn more in: Emerging Technologies to Increase Energy Efficiency and Decrease Indoor Pollution in University Campuses

7.

A machine learning technique that involves providing a machine with data that is labeled. Learn more in: Marketing and Artificial Intelligence: Personalization at Scale

8.

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. Learn more in: Machine Learning

9.

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. Learn more in: 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

10.

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. Learn more in: Data Mining and the KDD Process

11.

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. Learn more in: Using Sentiment Analytics to Understand Learner Experiences in Serious Games

12.

A technique of a Machine Learning algorithm. It uses known data to get some prediction about unknown data with the statistical model. Learn more in: Machine Learning in Computer Vision

13.

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 . Learn more in: Ensemble Clustering Data Mining and Databases

14.

A method in machine learning uses the model that has been trained to analyze the data. Learn more in: Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques

15.

The machine learning task of deducing a function from a set of labeled training data. Learn more in: Identification of Wireless Devices From Their Physical Layer Radio-Frequency Fingerprints

16.

A type of machine learning which uses a labeled dataset, such that the algorithm attempts to match the output labels based on input data. Learn more in: Introduction to Machine Learning as a New Methodological Framework for Performance Assessment

17.

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. Learn more in: First of All, Understand Data Analytics Context and Changes

18.

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. Learn more in: 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

19.

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. Learn more in: Artificial Intelligence and Machine Learning Education and Literacy: Teacher Training for Primary and Secondary Education Teachers

20.

Type of machine learning that uses a large set of training data (i.e., labeled data) to build the model. Naïve Bayes, k -nearest neighbors, and support vector machines are examples of supervised learning algorithms. Generally referred to as machine learning. Learn more in: Using Machine Learning to Locate Evidence More Efficiently: New Roles for Academic Research Librarians

21.

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. Learn more in: Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

22.

A machine learning algorithm that learns from labeled data included in a training set. Learn more in: Towards an Effective Imaging-Based Decision Support System for Skin Cancer

23.

The relationship between inputs and their outputs that allows to make a future prediction, uses labelled data. Learn more in: The Application of Machine Learning for Predicting Global Seismicity

24.

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. Learn more in: Learning Aided Digital Image Compression Technique for Medical Application

25.

A machine learning method that maps an input to an output based on the input-output pairs of data Learn more in: Predictive Modelling for Financial Fraud Detection Using Data Analytics: A Gradient-Boosting Decision Tree

26.

A learning strategy in which the desired output, or dependent attribute, is known. Learn more in: Artificial Neural Networks and Their Applications in Business

27.

A task of learning a function that maps input to the output based on example input-output pairs. Learn more in: Leveraging VR/AR/MR and AI as Innovative Educational Practices for “iGeneration” Students

28.

A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student. Learn more in: The Role of Artificial Intelligence in Cyber Security

29.

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. Learn more in: Pattern Recognition Methods

30.

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). Learn more in: Developing an Effective Classification Model for Medical Data Analysis

31.

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. Learn more in: AI Methods for Analyzing Microarray Data

32.

The field in machine learning which is concerned on the development of algorithms that learn functions from labelled data. Learn more in: Machine Learning for Smart Tourism and Retail

33.

A machine learning technique of predicting the value of a given function for any input based on labeled training data. Learn more in: Imbalanced Classification for Business Analytics

34.

One type of a machine learning task which intends to infer a function from labeled training data. Learn more in: Recommending Rating Values on Reviews for Designers

35.

It is machine learning algorithm in which the model learns from ample amount of available labeled data to predict the class of unseen instances. Learn more in: Natural Language Processing in Online Reviews

36.

A sub-category of machine learning that uses labelled data to infer relationships between the input and output. Learn more in: Machine Learning Algorithms in Human Gait Analysis

37.

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. Learn more in: Intelligent Slotting for the Warehouse

38.

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. Learn more in: Role of Artificial Intelligence in Cyber Security: A Useful Overview

39.

In supervised learning models (at their development stages) are provided with data/ examples on both input (predicator variables) and output (category) labels. Learn more in: Use of “Odds” in Bayesian Classifiers

40.

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. Learn more in: Automatic Detection of Emotion in Music: Interaction with Emotionally Sensitive Machines

41.

In supervised learning, a mathematical and statistical predictive model is constructed using a raw data set that is already tagged with correct labels. Learn more in: Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

42.

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. Learn more in: Machine Learning Approaches to Automated Medical Decision Support Systems

43.

Machine learning approaches often used for regression and classification. Learn more in: Concerning the Integration of Machine Learning Content in Mechatronics Curricula

44.

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. Learn more in: Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis: Transport Domain Adaptation of Sentiment Labelled Data Set

45.

A text mining algorithm, such as email spam filtering, that is developed and refined through the use of a training data set. Learn more in: Amplifying Participant Voices Through Text Mining

46.

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. Learn more in: Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services

47.

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. Learn more in: EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild

48.

Making predictions for samples that the learning model has not evaluate before by taking a set of labeled samples as training data. Learn more in: Comparison of Machine Learning Algorithms in Predicting the COVID-19 Outbreak

49.

It is an approach of Artificial Intelligence where computer algorithm is trained on input data that has been labeled for a particular output. Learn more in: A Meta-Analytical Review of Deep Learning Prediction Models for Big Data

50.

Is part of machine learning that uses the input data to predict the output patter with the help of conditions set by the programmer. Learn more in: Introduction to Artificial Intelligence

51.

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”. Learn more in: Applications of Artificial Neural Networks in Economics and Finance

52.

The use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. Learn more in: Artificial Intelligence in the Delivery of Mobile Tourism Services

53.

Supervised learning is the data mining task of inferring a function from labeled training data. Learn more in: Investigation on Deep Learning Approach for Big Data: Applications and Challenges

54.

One of the types of machine learning algorithm that trains that data based on both input and output. Learn more in: Analytics of User Behaviors on Twitter Using Machine Learning

55.

The set of learning algorithms in which the samples in the training dataset are all labelled. Learn more in: Active Learning with SVM

56.

Computers are educated on labelled training data and then used to predict output in supervised learning, a subset of machine learning. Learn more in: Deep Learning Approach for Detecting Customer Churn in Telecommunication Industry

57.

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. Learn more in: The Exploration of Autonomous Vehicles

58.

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. Learn more in: Optimization of Crime Scene Reconstruction Based on Bloodstain Patterns and Machine Learning Techniques

59.

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. Learn more in: An Overview of Applications of Artificial Intelligence Using Different Techniques, Algorithms, and Tools

60.

It is the machine learning technique in which the input and output are based on the input-output pairs. Learn more in: A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks

61.

A method of machine learning which requires human intervention at the starting or during the learning process. Learn more in: Advancements in Computer Aided Imaging Diagnostics

62.

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. Learn more in: Data Science Tools Application for Business Processes Modelling in Aviation

63.

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. Learn more in: Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya

64.

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 . Learn more in: Consensus Clustering

65.

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) Learn more in: Hierarchical Neuro-Fuzzy Systems Part I

66.

A learning strategy of developing an ANN in which the desired output, or dependent attribute, is known. Learn more in: Artificial Neural Networks for Business Analytics

67.

The machine learning task of inferring a function from labeled training data that consist of a set of training examples. Learn more in: A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

68.

A learning strategy in which the desired output, or dependent attribute, is known. Learn more in: Artificial Neural Networks and Data Science

69.

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. Learn more in: Machine Learning in Python: Diabetes Prediction Using Machine Learning

70.

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. Learn more in: Differential Learning Expert System in Data Management

71.

Class of machine learning algorithms that rely on the knowledge of an external supervisor in order to learn. Learn more in: The Role and Applications of Machine Learning in Future Self-Organizing Cellular Networks

72.

Supervised learning aims at developing a function for a set of labeled data and outputs. Learn more in: Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture

73.

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. Learn more in: Clubhouse Experience: Sentiment Analysis of an Alternative Platform From the Eyes of Classic Social Media Users

74.

The supervised learning technique aims to train an ML model using pre-labeled data. Learn more in: Machine Learning and Optimization Applications for Soft Robotics

75.

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. Learn more in: Artificial Neural Networks

76.

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. Learn more in: Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques

77.

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. Learn more in: Understanding Machine Learning Concepts

78.

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. Learn more in: Opinion Mining in Tourism: A Study on “Cappadocia Home Cooking” Restaurant

79.

It is a machine learning technique. It generates a function to match the inputs to the desired outputs. Learn more in: An Extensive Text Mining Study for the Turkish Language: Author Recognition, Sentiment Analysis, and Text Classification

80.

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). Learn more in: Class Prediction in Test Sets with Shifted Distributions

81.

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. Learn more in: Application of Machine Learning In Forensic Science