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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Yu, H., Kim, S. (2012). SVM Tutorial — Classification, Regression and Ranking. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_15
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DOI: https://doi.org/10.1007/978-3-540-92910-9_15
Publisher Name: Springer, Berlin, Heidelberg
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