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What is Supervised Machine Learning

Chapter 3
Handbook of Research on Innovations in Information Retrieval, Analysis, and Management
Given some example patterns and their true labels, a supervised machine learning technique finds a function which can predict the labels of unseen examples.
Published in Chapter:
From Tf-Idf to Learning-to-Rank: An Overview
Muhammad Ibrahim (Monash University, Australia) and Manzur Murshed (Federation University, Australia)
Copyright: © 2016 |Pages: 48
DOI: 10.4018/978-1-4666-8833-9.ch003
Abstract
Ranking a set of documents based on their relevances with respect to a given query is a central problem of information retrieval (IR). Traditionally people have been using unsupervised scoring methods like tf-idf, BM25, Language Model etc., but recently supervised machine learning framework is being used successfully to learn a ranking function, which is called learning-to-rank (LtR) problem. There are a few surveys on LtR in the literature; but these reviews provide very little assistance to someone who, before delving into technical details of different algorithms, wants to have a broad understanding of LtR systems and its evolution from and relation to the traditional IR methods. This chapter tries to address this gap in the literature. Mainly the following aspects are discussed: the fundamental concepts of IR, the motivation behind LtR, the evolution of LtR from and its relation to the traditional methods, the relationship between LtR and other supervised machine learning tasks, the general issues pertaining to an LtR algorithm, and the theory of LtR.
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More Results
Chapter 11
Explainable Safety Risk Management in Construction With Unsupervised Learning
Training ML algorithms with labeled input data for delivery of a particular task.
Published in Chapter: Explainable Safety Risk Management in Construction With Unsupervised Learning; From: Artificial Intelligence and Machine Learning Techniques for Civil Engineering
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Chapter 1
A Machine Learning Approach to Classify the Telecommunication Customers Based on Their Profitability
Supervised learning, often known as supervised machine learning, is artificial intelligence and machine learning subcategory. It uses labelled datasets to train algorithms that accurately classify data or predict outcomes defined it. As input data is fed into the model, the weights are adjusted until the model is correctly fitted during the cross-validation phase.
Published in Chapter: A Machine Learning Approach to Classify the Telecommunication Customers Based on Their Profitability; From: Social Customer Relationship Management (Social-CRM) in the Era of Web 4.0
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Chapter 19
Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing
A set of prediction algorithms that use labeled data to learn from it with the goal to categorize new unlabeled data instances.
Published in Chapter: Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing; From: Handbook of Research on Organizational Transformations through Big Data Analytics
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Chapter 89
Machine Learning Approach to Art Authentication
A type of machine learning for which labeled input data is used to train a model to determine an output classification.
Published in Chapter: Machine Learning Approach to Art Authentication; From: Encyclopedia of Data Science and Machine Learning
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Chapter 122
New Ensemble Machine Learning Method for Classification and Prediction on Gene Expression Data
Machine learning algorithms require training datasets with prior knowledge of class labels.
Published in Chapter: New Ensemble Machine Learning Method for Classification and Prediction on Gene Expression Data; From: Encyclopedia of Healthcare Information Systems
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Chapter 15
Building a Chatbot for Libraries
This type of machine learning happens using labeled datasets to train artificial intelligence algorithms to cluster datasets accurately.
Published in Chapter: Building a Chatbot for Libraries; From: Handbook of Research on Advancements of Contactless Technology and Service Innovation in Library and Information Science
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Chapter 7
Computer Aided Knowledge Discovery in Biomedicine
The use of class labels as prior knowledge to learn discriminative models from training examples consisting of feature vectors descriptive of the target class.
Published in Chapter: Computer Aided Knowledge Discovery in Biomedicine; From: Handbook of Research on Systems Biology Applications in Medicine
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Chapter 9
Exploring Public Perceptions of COVID-19 Vaccine Adverse Effects Through Social Media Analysis
In order to apply what they have learned in the past to new data and predict future events, supervised ML algorithms utilise labelled examples. Based on the analysis of a known training dataset, the learning technique creates an inferred function to forecast the output values. The system might provide goals for any new input after receiving enough training. In order to identify errors and enable the model to be corrected as necessary, the learning algorithm may also compare its output to the correct, intended result ( Alzubi et al., 2018 ).
Published in Chapter: Exploring Public Perceptions of COVID-19 Vaccine Adverse Effects Through Social Media Analysis; From: Handbook of Research on Advancements of Contactless Technology and Service Innovation in Library and Information Science
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Chapter 10
A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning
A computational method that is able to predict the output value of an unknown object based on the training of a set of training examples (input and output data).
Published in Chapter: A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning; From: Handbook of Research on Patient Safety and Quality Care through Health Informatics
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Chapter 2
The Use of Machine Learning in Libraries: How to Build a Book Recommender System
This type of machine learning happens by the use of labeled datasets to train artificial intelligence algorithms to cluster datasets accurately.
Published in Chapter: The Use of Machine Learning in Libraries: How to Build a Book Recommender System; From: Handbook of Research on Advancements of Contactless Technology and Service Innovation in Library and Information Science
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Chapter 168
Hybrid Machine Learning for Matchmaking in Digital Business Ecosystems
Supervised machine learning, is a subcategory of machine learning. This type of algorithm uses labeled sets of data, known as training data, to train models or produce an inferred function, allowing new samples of data to be classified or results to be accurately predicted. Therefore, the more data that is fed into the model, the more optimally it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross-validation process.
Published in Chapter: Hybrid Machine Learning for Matchmaking in Digital Business Ecosystems; From: Encyclopedia of Data Science and Machine Learning
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Chapter 4
Quality Improvement of Healthcare Services Through Data Analytics Processes
This is a type of machine learning approach where the objective is to build a model that can learn the relationship between the input variables (predictors) and the target variable (predicted) so that the model can make accurate predictions on new, unseen data. Predictive analytics (e.g. linear regression and decision tree techniques) is part of Supervised Machine Learning.
Published in Chapter: Quality Improvement of Healthcare Services Through Data Analytics Processes; From: Advancement in Business Analytics Tools for Higher Financial Performance
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