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Journal ArticleDOI

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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Citations
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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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Journal ArticleDOI
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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Proceedings ArticleDOI
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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Journal ArticleDOI
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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Proceedings Article
01 Jan 2009
TL;DR: This is the first paper coming up with a convergence rate for these algorithms without introducing unnecessarily restrictive assumptions, and it is general enough to cover the algorithms that are used in software packages like SVMTorch and (first or second order) LibSVM.
Abstract: We consider (a subclass of) convex quadratic optimization problems and analyze decomposition algorithms that perform, at least approximately, steepest-descent exact line search. We show that these algorithms, when implemented properly, are within ǫ of optimality after O(log 1/ǫ) iterations for strictly convex cost functions, and after O(1/ǫ) iterations in the general case. Our analysis is general enough to cover the algorithms that are used in software packages like SVMTorch and (first or second order) LibSVM. To the best of our knowledge, this is the first paper coming up with a convergence rate for these algorithms without introducing unnecessarily restrictive assumptions.

33 citations

Journal ArticleDOI
15 Jul 2001
TL;DR: In this paper, the authors show that directly using this base set as the working set leads to similar convergence (number of iterations) as using a base set first and then expanding it so that all indices are pairs.
Abstract: The dual formulation of support vector regression involves with two closely related sets of variables. When the decomposition method is used, many existing approaches use pairs of indices from these two sets as the working set. Basically they select a base set first and then expand it so that all indices are pairs. This makes the implementation different from that for support vector classification. In addition, a larger optimization sub-problem has to be solved in each iteration. In this paper from different aspects we demonstrate that there are no needs to do so. In particular we show that directly using this base set as the working set leads to similar convergence (number of iterations). Therefore, not only the program can be simpler, with a smaller working set and similar number of iterations, it can also be more efficient.

24 citations

Journal ArticleDOI
TL;DR: In this article, a new technique for working set selection in SMO-type decompositions is proposed for training support vector machines (SVMs), which is an important step in decomposition methods for training SVM.
Abstract: Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in SMO-type decomposit...

21 citations

Journal ArticleDOI
TL;DR: In this paper, a general decomposition algorithm that is uniformly applicable to every (suitably normalized) instance of Convex Quadratic Optimization and efficiently approaches an optimal solution is presented.
Abstract: We present a general decomposition algorithm that is uniformly applicable to every (suitably normalized) instance of Convex Quadratic Optimization and efficiently approaches an optimal solution. The number of iterations required to be within e of optimality grows linearly with 1/e and quadratically with the number m of variables. The working set selection can be performed in polynomial time. If we restrict our considerations to instances of Convex Quadratic Optimization with at most k0 equality constraints for some fixed constant k0 plus some so-called box-constraints (conditions that hold for most variants of SVM-optimization), the working set is found in linear time. Our analysis builds on a generalization of the concept of rate certifying pairs that was introduced by Hush and Scovel. In order to extend their results to arbitrary instances of Convex Quadratic Optimization, we introduce the general notion of a rate certifying q-set. We improve on the results by Hush and Scovel (2003) in several ways. First our result holds for Convex Quadratic Optimization whereas the results by Hush and Scovel are specialized to SVM-optimization. Second, we achieve a higher rate of convergence even for the special case of SVM-optimization (despite the generality of our approach). Third, our analysis is technically simpler. We prove furthermore that the strategy for working set selection which is based on rate certifying sets coincides with a strategy which is based on a so-called "sparse witness of sub-optimality". Viewed from this perspective, our main result improves on convergence results by List and Simon (2004) and Simon (2004) by providing convergence rates (and by holding under more general conditions).

20 citations

Journal ArticleDOI
TL;DR: The BDVal suite of programs are presented, that fully automate the construction of predictive classification models from high-throughput data and generate detailed reports about the model construction process.
Abstract: Summary: High-throughput data can be used in conjunction with clinical information to develop predictive models. Automating the process of developing, evaluating and testing such predictive models on different datasets would minimize operator errors and facilitate the comparison of different modeling approaches on the same dataset. Complete automation would also yield unambiguous documentation of the process followed to develop each model. We present the BDVal suite of programs that fully automate the construction of predictive classification models from high-throughput data and generate detailed reports about the model construction process. We have used BDVal to construct models from microarray and proteomics data, as well as from DNA-methylation datasets. The programs are designed for scalability and support the construction of thousands of alternative models from a given dataset and prediction task. Availability and Implementation: The BDVal programs are implemented in Java, provided under the GNU General Public License and freely available at http://bdval.campagnelab.org Contact: ude.llenroc.dem@3002caf

6 citations


"LIBSVM: A library for support vecto..." refers methods in this paper

  • ...Domain Computer vision Natural language processing Neuroimaging Bioinformatics Representative works LIBPMK [Grauman and Darrell 2005] Maltparser [Nivre et al. 2007] PyMVPA [Hanke et al. 2009] BDVal [Dorff et al. 2010] A typical use of LIBSVM involves two steps: .rst, training a dataset to obtain a model and second, using the model to predict information of a testing dataset....

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  • ...…Neuroimaging Bioinformatics Representative works LIBPMK [Grauman and Darrell 2005] Maltparser [Nivre et al. 2007] PyMVPA [Hanke et al. 2009] BDVal [Dorff et al. 2010] A typical use of LIBSVM involves two steps: .rst, training a dataset to obtain a model and second, using the model to predict…...

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  • ...BDVal: reproducible large-scale predictive model development and validation in high-throughput datasets....

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