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Machine learning
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=== Supervised learning === {{Main|Supervised learning}} [[File:Svm max sep hyperplane with margin.png|thumb|A [[support-vector machine]] is a supervised learning model that divides the data into regions separated by a [[linear classifier|linear boundary]]. Here, the linear boundary divides the black circles from the white.]] Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.<ref>{{cite book |last1=Russell |first1=Stuart J. |last2=Norvig |first2=Peter |title=Artificial Intelligence: A Modern Approach |date=2010 |publisher=Prentice Hall |isbn=9780136042594 |edition=Third|title-link=Artificial Intelligence: A Modern Approach }}</ref> The data, known as [[training data]], consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an [[array data structure|array]] or vector, sometimes called a [[feature vector]], and the training data is represented by a [[Matrix (mathematics)|matrix]]. Through [[Mathematical optimization#Computational optimization techniques|iterative optimisation]] of an [[Loss function|objective function]], supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.<ref>{{cite book |last1=Mohri |first1=Mehryar |last2=Rostamizadeh |first2=Afshin |last3=Talwalkar |first3=Ameet |title=Foundations of Machine Learning |date=2012 |publisher=The MIT Press |isbn=9780262018258}}</ref> An optimal function allows the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.<ref name="Mitchell-1997" /> Types of supervised-learning algorithms include [[active learning (machine learning)|active learning]], [[Statistical classification|classification]] and [[Regression analysis|regression]].<ref name="Alpaydin-2010">{{cite book|last=Alpaydin|first=Ethem|title=Introduction to Machine Learning|date=2010|publisher=MIT Press|isbn=978-0-262-01243-0|page=9|url=https://books.google.com/books?id=7f5bBAAAQBAJ|access-date=25 November 2018|archive-date=17 January 2023|archive-url=https://web.archive.org/web/20230117053338/https://books.google.com/books?id=7f5bBAAAQBAJ|url-status=live}}</ref> Classification algorithms are used when the outputs are restricted to a limited set of values, while regression algorithms are used when the outputs can take any numerical value within a range. For example, in a classification algorithm that filters emails, the input is an incoming email, and the output is the folder in which to file the email. In contrast, regression is used for tasks such as predicting a person's height based on factors like age and genetics or forecasting future temperatures based on historical data.<ref>{{Cite web |title=Lecture 2 Notes: Supervised Learning |url=https://www.cs.cornell.edu/courses/cs4780/2022sp/notes/LectureNotes02.html |access-date=1 July 2024 |website=www.cs.cornell.edu}}</ref> [[Similarity learning]] is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in [[ranking]], [[recommender system|recommendation systems]], visual identity tracking, face verification, and speaker verification.
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