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
A Review On Multi-Label Learning Algorithms
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms with relevant analyses and discussions.
Journal ArticleDOI
The Precision-Recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
Takaya Saito,Marc Rehmsmeier +1 more
TL;DR: It is shown that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity.
Journal ArticleDOI
Universal approximation using incremental constructive feedforward networks with random hidden nodes
TL;DR: This paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer.
Book ChapterDOI
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
TL;DR: SPP-Net as mentioned in this paper proposes a spatial pyramid pooling strategy, which can generate a fixed-length representation regardless of image size/scale, and achieves state-of-the-art performance in object detection.
Journal ArticleDOI
Random forest classifier for remote sensing classification
TL;DR: It is suggested that the random forest classifier performs equally well to SVMs in terms of classification accuracy and training time and the number of user‐defined parameters required byrandom forest classifiers is less than the number required for SVMs and easier to define.
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.