Twin Support Vector Machine: A review from 2007 to 2014
Divya Tomar,Sonali Agarwal +1 more
TLDR
This paper presents the research development of TWSVM in recent years and discusses the basic concept ofTWSVM, which is an emerging machine learning method suitable for both classification and regression problems.About:
This article is published in Egyptian Informatics Journal.The article was published on 2015-03-01 and is currently open access. It has received 85 citations till now. The article focuses on the topics: Relevance vector machine & Structured support vector machine.read more
Citations
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Journal ArticleDOI
A comparison of machine learning methods for cutting parameters prediction in high speed turning process
TL;DR: Support vector machines are arguably one of the most successful methods for data classification, but when using them in regression problems, literature suggests that their performance is no longer state-of-the-art.
Journal ArticleDOI
Intuitionistic Fuzzy Twin Support Vector Machines
TL;DR: This paper presents an intuitionistic FTSVM (IFTSVM) that combines the idea of intuitionistic fuzzy number with twin support vector machine (TSVM).
Journal ArticleDOI
One-class support vector classifiers: A survey
TL;DR: This survey comprises available algorithms, parameter estimation techniques, feature selection strategies, sample reduction methodologies, workability in distributed environment and application domains related to OCSVCs.
Journal ArticleDOI
Twin SVM-Based Classification of Alzheimer's Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA.
TL;DR: A novel approach for distinguishing AD from HC using dual-tree complex wavelet transforms (DTCWT), principal coefficients from the transaxial slices of MRI images, linear discriminant analysis, and twin support vector machine is proposed here.
Posted Content
A Novel Hybrid Kpca and SVM with ga Model for Intrusion Detection
Abstract: A novel support vector machine (SVM) model combining kernel principal component analysis (KPCA) with genetic algorithm (GA) is proposed for intrusion detection. In the proposed model, a multi-layer SVM classifier is adopted to estimate whether the action is an attack, KPCA is used as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. In order to reduce the noise caused by feature differences and improve the performance of SVM, an improved kernel function (N-RBF) is proposed by embedding the mean value and the mean square difference values of feature attributes in RBF kernel function. GA is employed to optimize the punishment factor C, kernel parameters @s and the tube size @? of SVM. By comparison with other detection algorithms, the experimental results show that the proposed model performs higher predictive accuracy, faster convergence speed and better generalization.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Proceedings ArticleDOI
Advances in kernel methods: support vector learning
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.
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