Algorithms that learn from labeled data a regression function.<\/p>\n
The following regression\u00a0learning algorithm are divided\u00a0into:<\/p>\n
<\/p>\n
Java class<\/strong>:\u00a0EpsilonSvmRegression<\/a><\/p>\n Source code<\/strong>:\u00a0EpsilonSvmRegression.java<\/a><\/p>\n Maven Project<\/strong>: kelp-core<\/p>\n JSON type<\/strong>:\u00a0epsilonSvmRegression<\/p>\n Description<\/strong>: It implements the \u03b5-Support Vector Regression learning algorithm. It is a learning algorithm for linear regression based on Support Vector Machines [Vapnik(1998)]. It relies on kernel functions. It is a Java porting of the library LIBSVM v3.17, written in C++ [Chang and Lin(2011)].<\/p>\n Parameters:<\/strong><\/p>\n Java class<\/strong>:\u00a0KernelizedPassiveAggressiveRegression<\/a><\/p>\n Source code<\/strong>:\u00a0KernelizedPassiveAggressiveRegression.java<\/a><\/p>\n Maven Project<\/strong>: kelp-additional-algorithms<\/p>\n JSON type<\/strong>:\u00a0kernelizedPA-R<\/p>\n Description<\/strong>: Online Passive-Aggressive Learning Algorithm for regression tasks (kernel-based version, proposed in [Crammer et al.(2006)]).<\/p>\n Parameters<\/strong>:<\/p>\n <\/p>\n<\/div>\n<\/div>\n<\/div>\n Java class<\/strong>: LibLinearRegression<\/a><\/p>\n Source code<\/strong>:\u00a0LibLinearRegression.java<\/a><\/p>\n Maven Project<\/strong>: kelp-additional-algorithms<\/p>\n JSON type<\/strong>: liblinearregression<\/p>\n Description<\/strong>: This class implements linear SVM regression trained using a coordinate descent algorithm [Fan et al.(2008)]. It operates in an explicit feature space (i.e., it does not rely on any kernel). This code has been adapted from the Java port of the original LIBLINEAR C++ sources.<\/p>\n Parameters<\/strong>:<\/p>\n Java class<\/strong>: LinearPassiveAggressiveRegression<\/a><\/p>\n Source code<\/strong>:\u00a0LinearPassiveAggressiveRegression.java<\/a><\/p>\n Maven Project<\/strong>: kelp-additional-algorithms<\/p>\n JSON type<\/strong>:\u00a0linearPA-R<\/p>\n Description<\/strong>: Online Passive-Aggressive Learning Algorithm for regression tasks (linear version, proposed in [Crammer et al.(2006)]).<\/p>\n Parameters<\/strong>:<\/p>\n <\/p>\n Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, and Yoram Singer.\u00a0On-line passive-aggressive algorithms<\/em>. Journal of Machine Learning Research, 7:551\u2013585, December 2006. ISSN 1532-4435.<\/p>\n Hsieh<\/span>, C.–<\/span>J., Chang<\/span>, K.–<\/span>W., Lin<\/span>, C.–<\/span>J., Keerthi<\/span>, S. S. and\u00a0Sundararajan<\/span>, S.\u00a0(2008). A Dual Coordinate Descent Method for Large–<\/span>scale Linear SVM.<\/em>\u00a0Proceedings of the 25th international conference on Machine learning –<\/span> ICML\u00a0’08 (pp<\/span>. 408–<\/span>415). New York, New York, USA: ACM Press.<\/p>\n R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin<\/b>. LIBLINEAR: A library for large linear classification <\/a>Journal of Machine Learning Research<\/a><\/i> 9(2008), 1871-1874.<\/p>\n <\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":" Algorithms that learn from labeled data a regression function. The following regression\u00a0learning algorithm are divided\u00a0into: Kernel Methods: methods operating in the\u00a0Reproducing Kernel Hilbert Space Linear Methods: methods operating in the explicit primal space Kernel Methods EpsilonSvmRegression Java class:\u00a0EpsilonSvmRegression Source code:\u00a0EpsilonSvmRegression.java Maven Project: kelp-core JSON type:\u00a0epsilonSvmRegression Description: It implements the \u03b5-Support Vector Regression learning algorithm. Read more about Regression Algorithms<\/span>[…]<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages\/798"}],"collection":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=798"}],"version-history":[{"count":12,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages\/798\/revisions"}],"predecessor-version":[{"id":914,"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=\/wp\/v2\/pages\/798\/revisions\/914"}],"wp:attachment":[{"href":"http:\/\/www.kelp-ml.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
\nKernelizedPassiveAggressiveRegression<\/h3>\n
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\u00a0Linear\u00a0Methods<\/span><\/strong><\/h1>\n
\nLibLinearRegression<\/h3>\n
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\nLinearPassiveAggressiveRegression<\/h3>\n
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\nReferences<\/h3>\n