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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NOXclass: prediction of protein-protein interaction types
TL;DR: NOXclass allows the interpretation and analysis of protein quaternary structures and generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available, according to the effect of the complex formation on the stability of the protomers.
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Automatic Classification for Pathological Prostate Images Based on Fractal Analysis
Po-Whei Huang,Cheng-Hsiung Lee +1 more
TL;DR: A computer-aided system to automatically grade pathological images according to Gleason grading system which is the most widespread method for histological grading of prostate tissues is presented and two feature extraction methods based on fractal dimension are proposed to analyze variations of intensity and texture complexity in regions of interest.
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Detection of Clinical Depression in Adolescents’ Speech During Family Interactions
TL;DR: The findings indicate the importance of nonlinear mechanisms associated with the glottal flow formation as cues for clinical depression in adolescents.
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Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set
TL;DR: Two classes of algorithms that can classify drivers as compliant or violating at road intersections are introduced, based on support vector machines and hidden Markov models, which are two very popular machine learning approaches that have been used successfully for classification in multiple disciplines.
Journal ArticleDOI
The role of balanced training and testing data sets for binary classifiers in bioinformatics.
Qiong Wei,Roland L. Dunbrack +1 more
TL;DR: This work shows that using balanced training data results in the highest balanced accuracy (the average of True Positive Rate and True Negative Rate), Matthews correlation coefficient, and area under ROC curves, no matter what the proportions of the two phenotypes are in the testing data.
References
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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
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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.