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2012 | OriginalPaper | Buchkapitel

SVM Tutorial — Classification, Regression and Ranking

verfasst von : Hwanjo Yu, Sungchul Kim

Erschienen in: Handbook of Natural Computing

Verlag: Springer Berlin Heidelberg

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Abstract

Support vector machines (SVMs) have been extensively researched in the data mining and machine learning communities for the last decade, and applied in various domains. They represent a set of supervised learning techniques that create a function from training data, which usually consists of pairs of an input object, typically vectors, and a desired output. SVMs learn a function that generates the desired output given the input, and the learned function can be used to predict the output of a new object. They belong to a family of generalized linear classifier where the classification (or boundary) function is a hyperplane in the feature space. This chapter introduces the basic concepts and techniques of SVMs for learning classification, regression, and ranking functions.

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Vorheriges Kapitel Neural Networks for Time-Series Forecasting

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Titel: SVM Tutorial — Classification, Regression and Ranking
verfasst von: Hwanjo Yu
Sungchul Kim
Verlag: Springer Berlin Heidelberg
Buch: Handbook of Natural Computing
Print ISBN: 978-3-540-92909-3

Electronic ISBN: 978-3-540-92910-9

Copyright-Jahr: 2012
DOI: https://doi.org/10.1007/978-3-540-92910-9_15

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