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http://ftp.cs.wisc.edu/machine-learning/shavlik-group/kunapuli.ecml10.pdf
Online Knowledge-Based Support Vector Machines Gautam Kunapuli∗, Kristin P. Bennett†, Amina Shabbeer†, Richard Maclin‡ and Jude Shavlik∗ ∗University of Wisconsin-Madison, †Rensselaer Polytechnic Insitute, and ‡University of Minnesota, Duluth Abstract. Prior knowledge, in the form of simple advice rules, can
https://link.springer.com/chapter/10.1007/978-3-642-15883-4_10
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and then receive the... Online Knowledge-Based Support Vector Machines SpringerLinkCited by: 23
https://www.academia.edu/7246076/Online_Knowledge-Based_Support_Vector_Machines
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and then receive the correct label (and learn from that information). The
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.5909
Abstract. Abstract. We propose a novel approach for incorporating prior knowledge into the online binary support vector classification problem. An existing advice-taking approach, when prior knowledge is in the form of polyhedral knowledge sets in input space of data, is via knowledge-based support vector machines (KB-SVMs).
https://en.wikipedia.org/wiki/Support-vector_machine
In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category ...
http://ftp.cs.wisc.edu/machine-learning/shavlik-group/kunapuli.ecml10.slides.pdf
Online Knowledge-Based Support Vector Machines Gautam Kunapuli1, Kristin P. Bennett2, Amina Shabbeer2, Richard Maclin 3and Jude W. Shavlik 1 1University of Wisconsin-Madison, USA 2Rensselaer Polytechnic Institute, USA 3University of Minnesota, Duluth, USA ECML 2010, Barcelona, Spain
https://www.pnas.org/content/97/1/262
Jan 04, 2000 · We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. …Cited by: 2982
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