Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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TLDR
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.Abstract:
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.read more
Citations
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Journal ArticleDOI
Asymptotic behaviors of support vector machines with Gaussian kernel
S. Sathiya Keerthi,Chih-Jen Lin +1 more
TL;DR: The behavior of the SVM classifier when these hyper parameters take very small or very large values is analyzed, which helps in understanding thehyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors.
Journal ArticleDOI
Rich Models for Steganalysis of Digital Images
Jessica Fridrich,Jan Kodovsky +1 more
TL;DR: A novel general strategy for building steganography detectors for digital images by assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters.
Journal ArticleDOI
Weisfeiler-Lehman Graph Kernels
TL;DR: A family of efficient kernels for large graphs with discrete node labels based on the Weisfeiler-Lehman test of isomorphism on graphs that outperform state-of-the-art graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime.
Journal ArticleDOI
Financial time series forecasting using support vector machines
TL;DR: The experimental results show that SVM provides a promising alternative to stock market prediction and the feasibility of applying SVM in financial forecasting is examined by comparing it with back-propagation neural networks and case-based reasoning.
Journal ArticleDOI
Using AUC and accuracy in evaluating learning algorithms
Jin Huang,Charles X. Ling +1 more
TL;DR: It is shown theoretically and empirically that AUC is a better measure (defined precisely) than accuracy and reevaluate well-established claims in machine learning based on accuracy using AUC and obtain interesting and surprising new results.
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.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
A Practical Guide to Support Vector Classication
TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
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.