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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Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network
Yushi Chen,Xing Zhao,Xiuping Jia +2 more
TL;DR: A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN) and a novel deep architecture is proposed, which combines the spectral-spatial FE and classification together to get high classification accuracy.
Journal ArticleDOI
Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds
TL;DR: An extension of a set previously used by the CheckMol software that covers in addition heterocyclic compound classes and periodic table groups is described, which demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models.
Journal ArticleDOI
Core Vector Machines: Fast SVM Training on Very Large Data Sets
TL;DR: This paper shows that many kernel methods can be equivalently formulated as minimum enclosing ball (MEB) problems in computational geometry and obtains provably approximately optimal solutions with the idea of core sets, and proposes the proposed Core Vector Machine (CVM) algorithm, which can be used with nonlinear kernels and has a time complexity that is linear in m.
Journal ArticleDOI
Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia,Katherine C. Elbert,Philip Adler,Casey Falk,Malia B. Wenny,Aurelio Mollo,Matthias Zeller,Sorelle A. Friedler,Joshua Schrier,Alexander J. Norquist +9 more
TL;DR: This work demonstrates an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites, and successfully predicted conditions for new organically Templated inorganic product formation.
Proceedings ArticleDOI
A dual coordinate descent method for large-scale linear SVM
TL;DR: A novel dual coordinate descent method for linear SVM with L1-and L2-loss functions that reaches an ε-accurate solution in O(log(1/ε)) iterations is presented.
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
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.
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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.