LIBSVM: A library for support vector machines
- Chih-Chung Chang, Chih-Jen Lin
- Computer ScienceTIST
- 1 April 2011
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
LIBLINEAR: A Library for Large Linear Classification
- Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin
- Computer ScienceJournal of machine learning research
- 1 June 2008
LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library…
A Practical Guide to Support Vector Classication
- Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin
- Business
- 2008
A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
A comparison of methods for multiclass support vector machines
- Chih-Wei Hsu, Chih-Jen Lin
- Computer ScienceIEEE Trans. Neural Networks
- 1 March 2002
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.
Projected Gradient Methods for Nonnegative Matrix Factorization
- Chih-Jen Lin
- Computer ScienceNeural Computation
- 1 October 2007
This letter proposes two projected gradient methods for nonnegative matrix factorization, both of which exhibit strong optimization properties and discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach.
A dual coordinate descent method for large-scale linear SVM
- Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Keerthi, S. Sundararajan
- Computer ScienceInternational Conference on Machine Learning
- 5 July 2008
A novel dual coordinate descent method for linear SVM with L1-and L2-loss functions that reaches an ε-accurate solution in O(log(1/ε)) iterations is presented.
Working Set Selection Using Second Order Information for Training Support Vector Machines
- Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin
- Computer ScienceJournal of machine learning research
- 1 December 2005
A new technique for working set selection in SMO-type decomposition methods that uses second order information to achieve fast convergence andoretical properties such as linear convergence are established.
Probability Estimates for Multi-class Classification by Pairwise Coupling
- Tingyao Wu, Chih-Jen Lin, R. C. Weng
- Computer ScienceJournal of machine learning research
- 9 December 2003
Two approaches for obtaining class probabilities can be reduced to linear systems and are easy to implement and shown conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods.
Combining SVMs with Various Feature Selection Strategies
- Yi-Wei Chen, Chih-Jen Lin
- Computer ScienceFeature Extraction
- 2006
This article investigates the performance of combining support vector machines (SVM) and various feature selection strategies. Some of them are filter-type approaches: general feature selection…
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
- S. Keerthi, Chih-Jen Lin
- Computer ScienceNeural Computation
- 1 July 2003
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.
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