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
Exudate-based diabetic macular edema detection in fundus images using publicly available datasets.
Luca Giancardo,Fabrice Meriaudeau,Thomas P. Karnowski,Yaquin Li,Seema Garg,Kenneth W. Tobin,Edward Chaum +6 more
TL;DR: A new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation is introduced, able to achieve diagnosis performance comparable to retina experts on the MESSIDOR with cross-dataset testing.
The MediaMill TRECVID 2006 semantic video search engine
Cees G. M. Snoek,J.C. van Gemert,Theo Gevers,Bouke Huurnink,Dennis C. Koelma,M. van Liempt,O. de Rooij,K.E.A. van de Sande,Frank J. Seinstra,Arnold W. M. Smeulders,Andrew Thean,Cor J. Veenman,Marcel Worring +12 more
TL;DR: The MediaMill Challenge 2006 as discussed by the authors divided the generic video indexing problem into a visual-only, textual only, early fusion, late fusion, and combined analysis experiment and the MediaMill team participated in two tasks: concept detection and search.
Journal ArticleDOI
Ensemble Sparse Classification of Alzheimer’s Disease
TL;DR: A local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification.
Journal ArticleDOI
Support vector machines and its applications in chemistry
TL;DR: Support vector machines (SVMs) are a promising machine learning method originally developed for pattern recognition problem based on structural risk minimization as discussed by the authors, which can be divided into two categories: support vector classification (SVC) machines and support vector regression (SVR) machines.
Journal ArticleDOI
Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology
TL;DR: A novel hybrid methodology that exploits the unique strength of the ARIMA and LSSVM models in forecasting carbon prices is proposed and particle swarm optimization is used to find the optimal parameters of L SSVM in order to improve the prediction accuracy.
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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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.