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Journal ArticleDOI

LIBSVM: A library for support vector machines

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.

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Citations
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Journal ArticleDOI

Age estimation using a hierarchical classifier based on global and local facial features

TL;DR: A new age estimation method using a hierarchical classifier method based on both global and local facial features is proposed, which was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.
Journal ArticleDOI

Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

TL;DR: A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented and "liveness" verification barriers are proposed as applications for which a significant amount of computation is avoided when estimating motion.
Patent

Method and system for detecting malicious and/or botnet-related domain names

TL;DR: In this paper, a method and system of detecting a malicious and/or botnet-related domain name, comprising of reviewing a domain name used in Domain Name System (DNS) traffic in a network is presented.
Journal ArticleDOI

Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering

TL;DR: A brief overview of the most common machine learning techniques employed in Raman, a guideline for new users to implement machine learning in their data analysis process, and an overview of modern applications of machine learning of Raman and SERS are provided.
Journal ArticleDOI

Robust Sparse Linear Discriminant Analysis

TL;DR: A novel feature extraction method called robust sparse linear discriminant analysis (RSLDA) is proposed to solve the above problems and achieves the competitive performance compared with other state-of-the-art feature extraction methods.
References
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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.