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
Tactile sensing in dexterous robot hands - Review
TL;DR: Current state-of-the-art of manipulation and grasping applications that involve artificial sense of touch that involve algorithms and tactile feedback-based control systems that exploit signals from the sensors are reviewed.
Journal ArticleDOI
Searching molecular structure databases with tandem mass spectra using CSI:FingerID.
TL;DR: This work presents CSI:FingerID, which combines fragmentation tree computation and machine learning for searching molecular structure databases using tandem MS data of small molecules, and is shown to improve on the competing methods for computational metabolite identification by a considerable margin.
Journal ArticleDOI
On the use of cross-validation for time series predictor evaluation
TL;DR: It is suggested that the use of a blocked form of cross-validation for time series evaluation became the standard procedure, thus using all available information and circumventing the theoretical problems.
A Comparison of Methods for Multi-class Support Vector Machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: These experiments indicate that the “one-against-one” and DAG methods are more suitable for practical use than the other methods, and show that for large problems methods by considering all data at once in general need fewer support vectors.
Journal ArticleDOI
A novel hybrid CNN-SVM classifier for recognizing handwritten digits
Xiao-Xiao Niu,Ching Y. Suen +1 more
TL;DR: A hybrid model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM) which have proven results in recognizing different types of patterns 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.
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.