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LIBSVM: A library for support vector machines
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail. Expand
LIBLINEAR: A Library for Large Linear Classification
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 libraryExpand
A Practical Guide to Support Vector Classication
A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM. Expand
A comparison of methods for multiclass support vector machines
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. Expand
Projected Gradient Methods for Nonnegative Matrix Factorization
  • Chih-Jen Lin
  • Mathematics, Computer Science
  • Neural 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. Expand
A dual coordinate descent method for large-scale linear SVM
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. Expand
Working Set Selection Using Second Order Information for Training Support Vector Machines
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. Expand
Probability Estimates for Multi-class Classification by Pairwise Coupling
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. Expand
Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions
This method uses the support vector machines trained by multiple feature vectors based on n‐peptide compositions to predict subcellular localization for Gram‐negative bacteria, and achieves the highest prediction rate ever reported. Expand
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
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. Expand