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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Proceedings ArticleDOI
Recognition using regions
TL;DR: This paper presents a unified framework for object detection, segmentation, and classification using regions using a generalized Hough voting scheme to generate hypotheses of object locations, scales and support, followed by a verification classifier and a constrained segmenter on each hypothesis.
Journal ArticleDOI
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
Julia Vogel,Bernt Schiele +1 more
TL;DR: A novel image representation is presented that renders it possible to access natural scenes by local semantic description by using a perceptually plausible distance measure that leads to a high correlation between the human and the automatically obtained typicality ranking.
Proceedings ArticleDOI
PrivBayes: private data release via bayesian networks
TL;DR: PrivBayes, a differentially private method for releasing high-dimensional data that circumvents the curse of dimensionality, and introduces a novel approach that uses a surrogate function for mutual information to build the model more accurately.
Journal ArticleDOI
Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models
TL;DR: In this paper, the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naive Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam) were investigated and compared.
Proceedings ArticleDOI
Confidence-weighted linear classification
TL;DR: Empirical evaluation on a range of NLP tasks show that the confidence-weighted linear classifiers introduced here improves over other state of the art online and batch methods, learns faster in the online setting, and lends itself to better classifier combination after parallel training.
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.