Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
Mei Liu,Yonghui Wu,Yukun Chen,Jingchun Sun,Zhongming Zhao,Xue-wen Chen,Michael E. Matheny,Hua Xu +7 more
TL;DR: A machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information is proposed.
Journal ArticleDOI
Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?
Taryn Hodgdon,Matthew D. F. McInnes,Nicola Schieda,Trevor A. Flood,Leslie R Lamb,Rebecca E. Thornhill +5 more
TL;DR: CT texture analysis can be used to accurately differentiate fp-AML from RCC on unenhanced CT images.
Journal ArticleDOI
Radius margin bounds for support vector machines with the RBF kernel
TL;DR: It is shown that finding a bound whose minima are in a region with small loo values may be more important than its tightness, and modified radius margin bounds for L1-SVM are proposed, where the original bound is applicable only to the hard-margin case.
Journal ArticleDOI
Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics
TL;DR: Both the simulation and experimental results show that the optimized KELM based fault diagnosis model can achieve high accuracy, reliability, and good generalization performance.
Proceedings ArticleDOI
Building semantic kernels for text classification using wikipedia
Pu Wang,Carlotta Domeniconi +1 more
TL;DR: This paper overcome the shortages of the BOW approach by embedding background knowledge derived from Wikipedia into a semantic kernel, which is then used to enrich the representation of documents.
References
More filters
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.