Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Advanced lectures on machine learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Neural Computation
Data mining techniques for cancer detection using serum proteomic profiling
Artificial Intelligence in Medicine
A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm
Expert Systems with Applications: An International Journal
K nearest neighbor reinforced expectation maximization method
Expert Systems with Applications: An International Journal
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The model of support vector machine (SVM) has been widely used to solve the problems of regression/classification. Here we propose a Bayesian approach to determining the separating hyperplane of an SVM, once its maximal margin is determined in the traditional way. This novel method minimizes the Bayes error in some derived direction. In the proposed model of b-SVM, all the parameters are estimated by the reversible jump Markov chain Monte Carlo (RJMCMC) strategies, and the location parameter of decision boundary is finally described by a posterior distribution. Tested by many independent random experiments of 2-fold cross validations, the experimental results on some high-throughput biodata sets demonstrate the promising performance and robustness of this novel classification method.