What is Supervised Learning

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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

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

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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

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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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

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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

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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

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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

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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

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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