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https://www3.nd.edu/~nchawla/papers/SPRINGER06b.pdf
22 Information Gain, Correlation and Support Vector Machines 465 H(S)=−p +(S)log 2 p +(S)−p−(S)logp−(S) p±(S) is the probability of a training example in the set Sto be of the posi- tive/negative class. We discretized continuous features using information the-
https://www.researchgate.net/publication/226211179_Information_Gain_Correlation_and_Support_Vector_Machines
22 Information Gain, Correlation and Support Vector Mac hines 469 22.4.2 Combining F eature Selection and Induction W e tried also a linear programming approach to SVM inspired by Bradley
https://link.springer.com/chapter/10.1007/978-3-540-35488-8_23
Abstract. We report on our approach, CBAmethod3E, which was submitted to the NIPS 2003 Feature Selection Challenge on Dec. 8, 2003. Our approach consists of combining filtering techniques for variable selection, information gain and feature correlation, with Support Vector Machines for induction.Cited by: 74
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.4876
Abstract. Summary. We report on our approach, CBAmethod3E, which was submitted to the NIPS 2003 Feature Selection Challenge on Dec. 8, 2003. Our approach consists of combining filtering techniques for variable selection, information gain and feature correlation, with Support Vector Machines for induction.
https://en.wikipedia.org/wiki/Feature_selection
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users,
https://pdfs.semanticscholar.org/c526/a3ecc5cb85fd0dc32cf8f2e0c8e057cf85a7.pdf
Feature Selection via Correlation Coefficient Clustering ... Coefficient, Support Vector Machines (SVMs), Machine Learning, Classification I. INTRODUCTION Feature selection aims to select the most problem- ... models with two information measurement: information gain …
https://www.researchgate.net/profile/Quanzhong_Liu/publication/220637867_Feature_selection_for_support_vector_machines_with_RBF_kernel/links/557ea92508aeea18b777e492.pdf
Feature selection for support vector machines kernel (SVM), a Nearest Neighbor with five neighbors (5NN), and a Nearest Neighbor with 10 neighbors (10NN) to evaluate feature subsets selected by ...
https://stats.stackexchange.com/questions/149662/is-support-vector-machine-sensitive-to-the-correlation-between-the-attributes
Is Support Vector Machine sensitive to the correlation between the attributes? Ask Question Asked 4 years, 7 months ago. ... I know that some of those attributes are highly correlated. Therefore my question is: is SVM sensitive to the correlation, or redundancy, between the features? Any reference? ... Support Vector Machines and the curse of ...
https://www.hindawi.com/journals/cmmm/2012/205025/
(iii) Support Vector Machines (SVMs) A support vector machine constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification. A good separation is achieved by the hyperplane that has the largest functional margin that is the distance to the nearest training data points of any class.Cited by: 11
https://core.ac.uk/display/23306223
Abstract. Summary. We report on our approach, CBAmethod3E, which was submitted to the NIPS 2003 Feature Selection Challenge on Dec. 8, 2003. Our approach consists of combining filtering techniques for variable selection, information gain and feature correlation, with Support Vector Machines for induction.
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