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
Interpretable classifiers using rules and bayesian analysis: building a better stroke prediction model
TL;DR: In this paper, a generative model called Bayesian rule lists (BRL) is proposed to predict the risk of stroke in patients with atrial fibrillation, which can be used to produce highly accurate and interpretable medical scoring systems.
Journal ArticleDOI
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals
TL;DR: This work proposes a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data that is applicable for different electrode placements and supports the introspection of results.
Book ChapterDOI
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
Siwei Lyu,Hany Farid +1 more
TL;DR: In this article, a wavelet-like decomposition is used to build higher-order statistical models of natural images and support vector machines are then used to discriminate between untouched and adulterated images.
Posted Content
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
TL;DR: This paper describes how it created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detects the sentimentof a term within a message (term-leveltask).
Journal ArticleDOI
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
TL;DR: In this article, a cost sensitive deep neural network (CoSen) is proposed to learn robust feature representations for both the majority and minority classes, which jointly optimizes the class-dependent costs and the neural network parameters.
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.