• Publications
  • Influence
LIBSVM: A library for support vector machines
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. LIBSVMExpand
  • 35,993
  • 3300
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
  • 7,026
  • 837
A Practical Guide to Support Vector Classication
Support vector machine (SVM) is a popular technique for classication. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but signicant steps.Expand
  • 6,265
  • 401
A comparison of methods for multiclass support vector machines
  • C. Hsu, C. Lin
  • Medicine, Computer Science
  • IEEE Trans. Neural Networks
  • 1 March 2002
Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods haveExpand
  • 5,787
  • 322
Projected Gradient Methods for Nonnegative Matrix Factorization
  • C. Lin
  • Computer Science, Medicine
  • Neural Computation
  • 1 October 2007
Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory andExpand
  • 1,371
  • 152
A dual coordinate descent method for large-scale linear SVM
In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such large-scale sparse data.Expand
  • 840
  • 117
Working Set Selection Using Second Order Information for Training Support Vector Machines
Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in SMO-typeExpand
  • 1,309
  • 111
Probability Estimates for Multi-class Classification by Pairwise Coupling
Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. BothExpand
  • 1,766
  • 97
Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions.
Gram-negative bacteria have five major subcellular localization sites: the cytoplasm, the periplasm, the inner membrane, the outer membrane, and the extracellular space. The subcellular location of aExpand
  • 500
  • 74
Combining SVMs with Various Feature Selection Strategies
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 selectionExpand
  • 918
  • 73