Modeling and integer programming techniques applied to propositional calculus
Computers and Operations Research - Special issue: Expert systems and operations research
The nature of statistical learning theory
The nature of statistical learning theory
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
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Regularized Knowledge-Based Kernel Machine
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
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The main objective of this paper is to present a kernel-based classification model based on mixed-integer programming (MIP). Most classification methods are based on models with continuous variables. The proposed MIP formulation is developed to discriminate between two classes of points, by minimizing the number of misclassified points, and the number of data points representing the separating hyperplane. Generalization for kernel-based classification is provided. The simplicity of the formulation makes it easy for domain expert to interpret. Preliminary computational results are reported using the single phase fluid flow data [14], which show that our model improves the support vector machine (SVM) solution.