LIBSVM: A library for support vector machines
TL;DR: 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.
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Journal Article•
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
47,974 citations
Cites methods from "LIBSVM: A library for support vecto..."
...While the package is mostly written in Python, it incorporates the C++ libraries LibSVM (Chang and Lin, 2001) and LibLinear (Fan et al....
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TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Abstract: More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
19,603 citations
Cites methods from "LIBSVM: A library for support vecto..."
...• Wrapper classifiers: allow the well known algorithms provided by the LibSVM [5] and LibLINEAR [9] thirdparty libraries to be used in WEKA....
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...Supported .le formats include WEKA s own ARFF format, CSV, LibSVM s format, and C4.5 s format....
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...6 is the ability to read and write data in the format used by the well known LibSVM and SVM-Light support vector machine implementations [5]....
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...This complements the new LibSVM and LibLIN-EAR wrapper classi.ers....
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...Wrapper classi.ers: allow the well known algorithms provided by the LibSVM [5] and LibLINEAR [9] thirdparty libraries to be used in WEKA....
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16 Jun 2012
TL;DR: The autonomous driving platform is used to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection, revealing that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world.
Abstract: Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net/datasets/kitti
11,283 citations
Cites methods from "LIBSVM: A library for support vecto..."
...We found that for the classification task SVMs [11] clearly outperform nearest neighbor classification....
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...All Classification Similarity SVM[11] 0....
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TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Abstract: In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.
10,696 citations
Journal Article•
TL;DR: LIBLINEAR is an open source library for large-scale linear classification that supports logistic regression and linear support vector machines and provides easy-to-use command-line tools and library calls for users and developers.
Abstract: LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets.
7,848 citations
References
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TL;DR: The asymptotic convergence of the algorithm used by the software SVM(light) and other later implementation is proved and the size of the working set can be any even number.
Abstract: The decomposition method is currently one of the major methods for solving support vector machines (SVM). Its convergence properties have not been fully understood. The general asymptotic convergence was first proposed by Chang et al. However, their working set selection does not coincide with existing implementation. A later breakthrough by Keerthi and Gilbert (2000, 2002) proved the convergence finite termination for practical cases while the size of the working set is restricted to two. In this paper, we prove the asymptotic convergence of the algorithm used by the software SVM/sup light/ and other later implementation. The size of the working set can be any even number. Extensions to other SVM formulations are also discussed.
245 citations
TL;DR: This paper connects this method to projected gradient methods and provides theoretical proofs for a version of decomposition methods and shows that this convergence proof is valid for general decomposition Methods if their working set selection meets a simple requirement.
Abstract: The support vector machine (SVM) is a promising technique for pattern recognition. It requires the solution of a large dense quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, very few methods can handle the memory problem and an important one is the "decomposition method." However, there is no convergence proof so far. We connect this method to projected gradient methods and provide theoretical proofs for a version of decomposition methods. An extension to bound-constrained formulation of SVM is also provided. We then show that this convergence proof is valid for general decomposition methods if their working set selection meets a simple requirement.
157 citations
"LIBSVM: A library for support vecto..." refers methods in this paper
...The convergence of decomposition methods was first studied in (Chang et al., 2000) but algorithms discussed there do not coincide with existing implementations....
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...However, its result applies only to decomposition methods discussed in (Chang et al., 2000) but not LIBSVM or other existing software....
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01 Jan 1998
TL;DR: The Support Vector Machine (SVM) program allows a user to carry out pattern recognition and regression estimation, using support vector techniques on some given data.
Abstract: The Support Vector Machine (SVM) is a new type of learning machine. The SVM is a general architecture that can be applied to pattern recognition, regression estimation and other problems. The following researchers were involved in the development of the SVM: The Support Vector Machine (SVM) program allows a user to carry out pattern recognition and regression estimation, using support vector techniques on some given data. If you have any questions not answered by the documentation, you can e-mail us at:
145 citations
"LIBSVM: A library for support vecto..." refers methods in this paper
...Some work on this method are, for example, (Osuna et al., 1997b; Joachims, 1998; Platt, 1998; Saunders et al., 1998)....
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TL;DR: The asymptotic convergence of C.-J.
Abstract: The asymptotic convergence of C.-J. Lin (2001) can be applied to a modified SMO (sequential minimal optimization) algorithm by S.S. Keerthi et al. (2001) with some assumptions. The author shows that for this algorithm those assumptions are not necessary.
134 citations
Proceedings Article•
29 Nov 1999TL;DR: In this article, the authors show that the recently proposed variant of the Support Vector machine (SVM) algorithm, known as ν-SVM, can be interpreted as a maximal separation between subsets of the convex hulls of the data.
Abstract: We show that the recently proposed variant of the Support Vector machine (SVM) algorithm, known as ν-SVM, can be interpreted as a maximal separation between subsets of the convex hulls of the data, which we call soft convex hulls. The soft convex hulls are controlled by choice of the parameter ν. If the intersection of the convex hulls is empty, the hyperplane is positioned halfway between them such that the distance between convex hulls, measured along the normal, is maximized; and if it is not, the hyperplane's normal is similarly determined by the soft convex hulls, but its position (perpendicular distance from the origin) is adjusted to minimize the error sum. The proposed geometric interpretation of ν-SVM also leads to necessary and sufficient conditions for the existence of a choice of ν for which the ν-SVM solution is nontrivial.
124 citations