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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Proceedings Article
DiSCO: Distributed Optimization for Self-Concordant Empirical Loss
Yuchen Zhang,Xiao Lin +1 more
TL;DR: The algorithm is based on an inexact damped Newton method, where the inexact Newton steps are computed by a distributed preconditioned conjugate gradient method, and its iteration complexity and communication efficiency for minimizing self-concordant empirical loss functions are analyzed.
Journal ArticleDOI
SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas
TL;DR: Experiments suggest that the proposed object-based classification method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.
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Online feature selection using grafting
Simon Perkins,James Theiler +1 more
TL;DR: It is argued that existing feature selection methods do not perform well in this scenario, and a promising alternative method is described, based on a stagewise gradient descent technique which is called grafting.
Proceedings ArticleDOI
Estimating the prevalence of deception in online review communities
TL;DR: In this article, the authors propose a generative model of deception which, in conjunction with a deception classifier, is used to explore the prevalence of deception in six popular online review communities: Expedia, Hotels.com, Orbitz, Priceline, TripAdvisor, and Yelp.
Posted Content
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification
TL;DR: In this article, a decision fusion model is proposed to aggregate patch-level predictions given by patchlevel CNNs, which can outperform an image-based CNN on WSIs.
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
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.