Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Support Vector Machine for Regression and Applications to Financial Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Multicategory Proximal Support Vector Machine Classifiers
Machine Learning
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of smoothing technique on twin support vector machines
Pattern Recognition Letters
IEEE Transactions on Knowledge and Data Engineering
Cancer Classification from Gene Expression Data by NPPC Ensemble
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
A study on SMO-type decomposition methods for support vector machines
IEEE Transactions on Neural Networks
Reduced Support Vector Machines: A Statistical Theory
IEEE Transactions on Neural Networks
Improvements on Twin Support Vector Machines
IEEE Transactions on Neural Networks
A regularization for the projection twin support vector machine
Knowledge-Based Systems
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
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The recently proposed twin parametric-margin support vector machine, denoted by TPMSVM, gains good generalization and is suitable for many noise cases. However, in the TPMSVM, it solves two dual quadratic programming problems (QPPs). Moreover, compared with support vector machine (SVM), TPMSVM has at least four regularization parameters that need regulating, which affects its practical applications. In this paper, we increase the efficiency of TPMSVM from two aspects. First, by introducing a quadratic function, we directly optimize a pair of QPPs of TPMSVM in the primal space, called STPMSVM for short. Compared with solving two dual QPPs in the TPMSVM, STPMSVM can obviously improve the training speed without loss of generalization. Second, a genetic algorithm GA-based model selection for STPMSVM in the primal space is suggested. The GA-based STPMSVM can not only select the parameters efficiently, but also provide discriminative feature selection. Computational results on several synthetic as well as benchmark datasets confirm the great improvements on the training process of our GA-based STPMSVM.