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
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
Zhuowen Tu,Xiang Bai +1 more
TL;DR: The scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems, including high-level vision and medical image segmentation problems.
Proceedings Article
Discriminating Gender on Twitter
TL;DR: The construction of a large, multilingual dataset labeled with gender is described and statistical models for determining the gender of uncharacterized Twitter users are investigated, and several different classifier types are explored.
Journal ArticleDOI
Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
Allen Zhang,Allen Zhang,Kelvin C. P. Wang,Kelvin C. P. Wang,Baoxian Li,Enhui Yang,Xianxing Dai,Yi Peng,Yue Fei,Yang Liu,Joshua Q. Li,Cheng Chen +11 more
TL;DR: The CrackNet, an efficient architecture based on the Convolutional Neural Network, is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy.
Proceedings ArticleDOI
Face spoofing detection from single images using micro-texture analysis
TL;DR: This work presents a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print, and analyzes the texture of the facial images using multi-scale local binary patterns (LBP).
Journal ArticleDOI
OpenMS – An open-source software framework for mass spectrometry
Marc Sturm,Andreas Bertsch,Clemens Gröpl,Andreas Hildebrandt,Rene Hussong,Eva Lange,Nico Pfeifer,Ole Schulz-Trieglaff,Alexandra Zerck,Knut Reinert,Oliver Kohlbacher +10 more
TL;DR: This work presents OpenMS, a software framework for rapid application development in mass spectrometry designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis.
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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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.