LIBSVM: A library for support vector machines

doi:10.1145/1961189.1961199

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LIBSVM: A library for support vector machines

Journal Article•DOI•

LIBSVM: A library for support vector machines

Chih-Chung Chang¹, Chih-Jen Lin¹•Institutions (1)

National Taiwan University¹

06 May 2011-ACM Transactions on Intelligent Systems and Technology (ACM)-Vol. 2, Iss: 3, pp 27

TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

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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•DOI•

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

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Giang Nguyen¹, Stefan Dlugolinsky¹, Martin Bobák¹, Viet Tran¹, Álvaro López García², Ignacio Heredia², Peter Malik¹, Ladislav Hluchý¹ - Show less +4 more•Institutions (2)

Slovak Academy of Sciences¹, Spanish National Research Council²

19 Jan 2019-Artificial Intelligence Review

TL;DR: This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software that is capable of scaling computation effectively and efficiently in the era of Big Data.

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Abstract: The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.

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443 citations

Cites background from "LIBSVM: A library for support vecto..."

...The ML group at National Taiwan University also provides support (in early stages) for MPI LibLinear, which is an extension of LibLinear for distributed environments and for Spark LibLinear, which is Spark implementation based on LibLinear and integrated with Hadoop distributed file system (NTU 2018)....
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...It was developed starting in 2007 at National Taiwan University (Fan et al. 2008; LibLinear 2018)....
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...Its development started in 2000 at National Taiwan University (Chang and Lin 2011; LibSVM 2018)....
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Journal Article•DOI•

Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines

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Yulan Liang¹, Michelle L. Reyes¹, John D. Lee¹•Institutions (1)

University of Iowa¹

01 Jun 2007-IEEE Transactions on Intelligent Transportation Systems

TL;DR: In this article, a real-time approach for detecting cognitive distraction using drivers' eye movements and driving performance data was proposed. But the results showed that the SVM models were able to detect driver distraction with an average accuracy of 81.1%, outperforming more traditional logistic regression models.

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Abstract: As use of in-vehicle information systems (IVISs) such as cell phones, navigation systems, and satellite radios has increased, driver distraction has become an important and growing safety concern. A promising way to overcome this problem is to detect driver distraction and adapt in-vehicle systems accordingly to mitigate such distractions. To realize this strategy, this paper applied support vector machines (SVMs), which is a data mining method, to develop a real-time approach for detecting cognitive distraction using drivers' eye movements and driving performance data. Data were collected in a simulator experiment in which ten participants interacted with an IVIS while driving. The data were used to train and test both SVM and logistic regression models, and three different model characteristics were investigated: how distraction was defined, which data were input to the model, and how the input data were summarized. The results show that the SVM models were able to detect driver distraction with an average accuracy of 81.1%, outperforming more traditional logistic regression models. The best performing model (96.1% accuracy) resulted when distraction was defined using experimental conditions (i.e., IVIS drive or baseline drive), the input data were comprised of eye movement and driving measures, and these data were summarized over a 40-s window with 95% overlap of windows. These results demonstrate that eye movements and simple measures of driving performance can be used to detect driver distraction in real time. Potential applications of this paper include the design of adaptive in-vehicle systems and the evaluation of driver distraction

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439 citations

Journal Article•DOI•

Beware of docking

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Yu-Chian Chen¹•Institutions (1)

China Medical University (Taiwan)¹

01 Feb 2015-Trends in Pharmacological Sciences

TL;DR: This review presents some evidence to show that the docking might be questionable, despite a high score, in some cases, and some cases the accuracy of docking can even change from 0% to 92.66%.

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435 citations

Journal Article•DOI•

Perception, Planning, Control, and Coordination for Autonomous Vehicles

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Scott Pendleton, Hans Andersen, Xinxin Du, Xiaotong Shen, Malika Meghjani, You Hong Eng, Daniela Rus, Marcelo H. Ang - Show less +4 more

17 Feb 2017

TL;DR: In this paper, the authors provide a general overview of the recent developments in the realm of autonomous vehicle software systems, and discuss the fundamental components of the software, as well as recent developments of each area.

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Abstract: Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efﬁciency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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434 citations

Cites methods from "LIBSVM: A library for support vecto..."

...Each point was then assigned with three types of attributes (linear, planar, and spherical) using LIBSVM [57] by taking the geometric features as input....
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Proceedings Article•DOI•

Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark

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Abhinav Dhall¹, Roland Goecke², Simon Lucey³, Tom Gedeon¹•Institutions (3)

Australian National University¹, University of Canberra², Commonwealth Scientific and Industrial Research Organisation³

01 Nov 2011

TL;DR: A person independent training and testing protocol for expression recognition as part of the BEFIT workshop is proposed and a new static facial expression database Static Facial Expressions in the Wild (SFEW) is presented.

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Abstract: Quality data recorded in varied realistic environments is vital for effective human face related research. Currently available datasets for human facial expression analysis have been generated in highly controlled lab environments. We present a new static facial expression database Static Facial Expressions in the Wild (SFEW) extracted from a temporal facial expressions database Acted Facial Expressions in the Wild (AFEW) [9], which we have extracted from movies. In the past, many robust methods have been reported in the literature. However, these methods have been experimented on different databases or using different protocols within the same databases. The lack of a standard protocol makes it difficult to compare systems and acts as a hindrance in the progress of the field. Therefore, we propose a person independent training and testing protocol for expression recognition as part of the BEFIT workshop. Further, we compare our dataset with the JAFFE and Multi-PIE datasets and provide baseline results.

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434 citations

Cites methods from "LIBSVM: A library for support vecto..."

...For classification, we used a support vector machine [5]....
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...(2) We used a five-fold cross validation script [5], which creates five subsets of the dataset....
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References

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Journal Article•DOI•

Support-Vector Networks

[...]

Corinna Cortes¹, Vladimir Vapnik¹•Institutions (1)

Bell Labs¹

15 Sep 1995-Machine Learning

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.

...read moreread less

Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

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37,861 citations

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

...{1,-1}, C-SVC [Boser et al. 1992; Cortes and Vapnik 1995] solves 4LIBSVM Tools: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools. the following primal optimization problem: l t min 1 w T w +C .i (1) w,b,. 2 i=1 subject to yi(w T f(xi) +b) =1 -.i, .i =0,i =1,...,l, where f(xi)maps xi into a…...
[...]

Statistical learning theory

[...]

Vladimir Vapnik

01 Jan 1998

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.

...read moreread less

Abstract: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. 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.

...read moreread less

26,531 citations

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

...Under given parameters C > 0and E> 0, the standard form of support vector regression [Vapnik 1998] is ll tt 1 T min w w + C .i + C .i * w,b,.,. * 2 i=1 i=1 subject to w T f(xi) + b- zi = E + .i, zi - w T f(xi) - b = E + .i * , * .i,.i = 0,i = 1,...,l....
[...]
...It can be clearly seen that C-SVC and one-class SVM are already in the form of problem (11)....
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..., l, in two classes, and a vector y ∈ Rl such that yi ∈ {1,−1}, C-SVC (Cortes and Vapnik, 1995; Vapnik, 1998) solves the following primal problem:...
[...]
...Then, according to the SVM formulation, svm train one calls a corresponding subroutine such as solve c svc for C-SVC and solve nu svc for ....
[...]
...Note that b of C-SVC and E-SVR plays the same role as -. in one-class SVM, so we de.ne ....
[...]

Proceedings Article•DOI•

A training algorithm for optimal margin classifiers

[...]

Bernhard E. Boser¹, Isabelle Guyon², Vladimir Vapnik²•Institutions (2)

University of California, Berkeley¹, Bell Labs²

01 Jul 1992

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.

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Abstract: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.

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11,211 citations

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

...It can be clearly seen that C-SVC and one-class SVM are already in the form of problem (11)....
[...]
...Then, according to the SVM formulation, svm train one calls a corresponding subroutine such as solve c svc for C-SVC and solve nu svc for ....
[...]
...Note that b of C-SVC and E-SVR plays the same role as -. in one-class SVM, so we de.ne ....
[...]
...In Section 2, we describe SVM formulations supported in LIBSVM: C-Support Vector Classi.cation (C-SVC), ....
[...]
...{1,-1}, C-SVC [Boser et al. 1992; Cortes and Vapnik 1995] solves 4LIBSVM Tools: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools. the following primal optimization problem: l t min 1 w T w +C .i (1) w,b,. 2 i=1 subject to yi(w T f(xi) +b) =1 -.i, .i =0,i =1,...,l, where f(xi)maps xi into a higher-dimensional space and C > 0 is the regularization parameter....
[...]

A Practical Guide to Support Vector Classication

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Hsu Chih-Wei, Chih-Chung Chang¹, Chih-Jen Lin•Institutions (1)

National Taiwan University¹

01 Jan 2008

TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.

...read moreread less

Abstract: Support vector machine (SVM) is a popular technique for classication. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but signicant steps. In this guide, we propose a simple procedure, which usually gives reasonable results.

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7,069 citations

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

...A Simple Example of Running LIBSVM While detailed instructions of using LIBSVM are available in the README file of the package and the practical guide by Hsu et al. [2003], here we give a simple example....
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...For instructions of using LIBSVM, see the README file included in the package, the LIBSVM FAQ,3 and the practical guide by Hsu et al. [2003]. LIBSVM supports the following learning tasks....
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Journal Article•DOI•

A comparison of methods for multiclass support vector machines

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Hsu Chih-Wei¹, Chih-Jen Lin¹•Institutions (1)

National Taiwan University¹

01 Mar 2002-IEEE Transactions on Neural Networks

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.

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Abstract: Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.

...read moreread less

6,562 citations