Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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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Journal ArticleDOI
3D analysis of facial morphology
Peter Hammond,Tim J. Hutton,J E Allanson,Linda E. Campbell,Raoul C.M. Hennekam,Sean B. Holden,Michael A. Patton,Adam Shaw,I. Karen Temple,Matthew Trotter,Kieran C. Murphy,Robin M. Winter +11 more
TL;DR: Dense surface models can be used to analyze 3D facial morphology by establishing a correspondence of thousands of points across each 3D face image and provide dramatic visualizations of face‐shape variation with potential for training physicians to recognize the key components of particular syndromes.
Journal ArticleDOI
An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine
TL;DR: A hybrid feature selection strategy based on genetic algorithm and support vector machine (GA-SVM) formed a wrapper to search for the best combination of bands with higher classification accuracy, which reduced the computational cost of the genetic algorithm.
Proceedings Article
Classifying Political Orientation on Twitter: It’s Not Easy!
Raviv Cohen,Derek Ruths +1 more
TL;DR: Standard techniques for inferring political orientation show that methods which previously reported greater than 90% inference accuracy, actually achieve barely 65% accuracy on normal users, and show that classifiers cannot be used to classify users outside the narrow range of political orientation on which they were trained.
Journal ArticleDOI
Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images
TL;DR: This article exploits the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images by proposing a novel kNN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously.
Journal ArticleDOI
Solar Power Prediction Based on Satellite Images and Support Vector Machine
TL;DR: In this article, a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme was proposed to forecast the motion vectors of clouds by utilizing satellite images of atmospheric motion vectors (AMVs).
References
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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
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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.