Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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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
Borderline over-sampling for imbalanced data classification
TL;DR: This paper proposes a new method for dealing with imbalanced data sets by over-sampling the borderline minority class instances by using a Support Vector Machine (SVM) classifier to predict future instances.
Journal ArticleDOI
The XENON100 dark matter experiment
Elena Aprile,K. Arisaka,F. Arneodo,A. Askin,Laura Baudis,A. Behrens,Ethan Brown,João Cardoso,B. Choi,David B. Cline,S. Fattori,A. D. Ferella,Karl Giboni,A. Kish,C. W. Lam,R.F. Lang,K. E. Lim,J. A. M. Lopes,T. Marrodán Undagoitia,Y. Mei,A. J. Melgarejo Fernandez,Kaixuan Ni,Uwe Oberlack,S. E. A. Orrigo,E. Pantic,Guillaume Plante,A. C. C. Ribeiro,R. Santorelli,J.M.F. dos Santos,Marc Schumann,P. Shagin,A. Teymourian,E. Tziaferi,Hui Wang,Masaki Yamashita +34 more
TL;DR: The XENON100 dark matter experiment uses liquid xenon (LXe) in a time projection chamber (TPC) to search for xenon nuclear recoils resulting from the scattering of dark matter Weakly Interacting Massive Particles (WIMPs) as discussed by the authors.
Proceedings Article
An Asynchronous Parallel Stochastic Coordinate Descent Algorithm
TL;DR: In this article, an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions is proposed, which achieves a linear convergence rate on functions that satisfy an essential strong convexity property and a sublinear rate on general convex functions.
Journal ArticleDOI
Elderly activities recognition and classification for applications in assisted living
TL;DR: This paper proposes an activity recognition and classification method for detection of Activities of Daily Livings of an elderly person using small, low-cost, non-intrusive non-stigmatize wrist worn sensors and demonstrates that the proposed method can achieve a high classification rate.
Journal ArticleDOI
Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective
TL;DR: It is shown that the off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-Iris-Thousand.
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.
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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.