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

Machine Learning Methods for Attack Detection in the Smart Grid

TL;DR: Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.
Proceedings ArticleDOI

Face recognition with learning-based descriptor

TL;DR: This work proposes a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations of the matching face pair, and finds that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor.
Proceedings ArticleDOI

Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera

TL;DR: A filtering method to extract STIPs from depth videos (called DSTIP) that effectively suppress the noisy measurements is presented and a novel depth cuboid similarity feature (DCSF) is built to describe the local 3D depth cuboids around the DSTips with an adaptable supporting size.
Journal ArticleDOI

Comparison of multivariate classifiers and response normalizations for pattern-information fMRI.

TL;DR: Compared classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T is compared and linear decoders based on t-value patterns may perform best.
Journal ArticleDOI

RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices

TL;DR: RT-Fall exploits the phase and amplitude of the fine-grained Channel State Information accessible in commodity WiFi devices, and for the first time fulfills the goal of segmenting and detecting the falls automatically in real-time, which allows users to perform daily activities naturally and continuously without wearing any devices on the body.
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.