scispace - formally typeset
Journal ArticleDOI

LIBSVM: A library for support vector machines

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

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Effects of institutional changes on land use: agricultural land abandonment during the transition from state-command to market-driven economies in post-Soviet Eastern Europe

TL;DR: In this article, the authors studied the effects of institutional changes on agricultural land abandonment in different countries of Eastern Europe and the former Soviet Union after the collapse of socialism and found that institutional settings play a key role in shaping land cover and land use.
Journal ArticleDOI

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

TL;DR: The results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable and SVM classification technique is suitable for real-time classification of sEMG signals.
Proceedings Article

Generating Natural-Language Video Descriptions Using Text-Mined Knowledge

TL;DR: This work combines the output of state-of-the-art object and activity detectors with "real-world" knowledge to select the most probable subject-verb-object triplet for describing a video, and shows that this knowledge, automatically mined from web-scale text corpora, enhances the triplet selection algorithm by providing it contextual information and leads to a four-fold increase in activity identification.
Journal ArticleDOI

CpG Island Methylation in Human Lymphocytes Is Highly Correlated with DNA Sequence, Repeats, and Predicted DNA Structure

TL;DR: It is suggested that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing C pG islands for DNA methylation.
Journal ArticleDOI

New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes

TL;DR: Two new statistical learning methods for estimating the optimal DTR are introduced, termed backward outcome weighted learning (BOWL) and simultaneous outcome weightedlearning (SOWL), and it is proved that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules.
References
More filters
Journal ArticleDOI

Support-Vector Networks

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

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.