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http://jmlr.csail.mit.edu/papers/volume9/claeskens08a/claeskens08a.pdf
Support vector machines for classification have the advantage that the curse of dimensionality is circumvented. It has been shown that a reduction of the dimension of the input space leads to even better results. For this purpose, we propose two information criteria which can be computed directly from the definition of the support vector machine.
https://experts.umn.edu/en/publications/a-consistent-information-criterion-for-support-vector-machines-in
Information criteria have been popularly used in model selection and proved to possess nice theoretical properties. For classification, Claeskens et al. (2008) proposed support vector machine information criterion for feature selection and provided encouraging numerical evidence.Cited by: 9
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3043755/
Support Vector Machine . Support vector machine is based on the structural risk minimization principle. It is reported that SVM outperforms other classifiers in many studies. 21, 22 The SVM approach enjoys many attributes. It is less computationally intense in comparison to artificial neural networks.Cited by: 77
https://www.amazon.com/Support-Machines-Information-Science-Statistics/dp/0387772413
Support Vector Machines (Information Science and Statistics) [Ingo Steinwart, Andreas Christmann] on Amazon.com. *FREE* shipping on qualifying offers. Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and ...Cited by: 2281
http://jmlr.csail.mit.edu/papers/volume3/rakotomamonjy03a/rakotomamonjy03a.pdf
ness of these criteria. Results show that the criterion based on weight vector derivative achieves good results and performs consistently well over the datasets we used. Keywords: support vector machines, kernels, variable selection, sensitivity. 1. Introduction Nowadays, many practical pattern recognition tasks infer knowledge from example ...
https://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html
Introduction to Support Vector Machines ... predict to test its performance. What is a SVM?¶ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, ... We use here a couple of methods to obtain information about the support vectors.
https://www.sciencedirect.com/science/article/pii/S0925231215005044
Dec 02, 2015 · Wind speed prediction using reduced support vector machines with feature selection. ... Support Vector Machine for data regression (SVR) can be a more powerful learning model with better generalization capability. The regularization term used in the cost function avoids over fitting, which is a favorable feature for forecasting the wind speed. ...Cited by: 87
https://www.springer.com/gp/book/9781849960977
A comprehensive resource for the use of Support Vector Machines (SVMs) in Pattern Classification; Takes the unique approach of focusing on classification …Author: Shigeo Abe
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.210.6913
Support vector machines for classification have the advantage that the curse of dimensionality is circumvented. It has been shown that a reduction of the dimension of the input space leads to even better results. For this purpose, we propose two information criteria which can be computed directly from the definition of the support vector machine.
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