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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Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.
TL;DR: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs) and results indicate that the method may be useful in the computer-aided detection ofmonary nodules using PET/ CT images.
Journal ArticleDOI
Multiple classifier systems for robust classifier design in adversarial environments
TL;DR: This paper focuses on a strategy recently proposed in the literature to improve the robustness of linear classifiers to adversarial data manipulation, and experimentally investigates whether it can be implemented using two well known techniques for the construction of multiple classifier systems, namely, bagging and the random subspace method.
Proceedings ArticleDOI
Failure Prediction in IBM BlueGene/L Event Logs
TL;DR: This study collects detailed event logs from IBM BlueGene/L, which has 128 K processors, and is currently the fastest supercomputer in the world, and shows that the customized nearest neighbor approach can outperform RIPPER and SVMs in terms of both coverage and precision.
Journal ArticleDOI
GLISTR: Glioma Image Segmentation and Registration
Ali Gooya,Kilian M. Pohl,Michel Bilello,Luigi Cirillo,George Biros,Elias R. Melhem,Christos Davatzikos +6 more
TL;DR: A generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumor as well as healthy tissue labels is presented.
Journal ArticleDOI
Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information
TL;DR: In this article, a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression is proposed for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market.
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.