scispace - formally typeset
Topic

Support vector machine

About: Support vector machine is a(n) research topic. Over the lifetime, 73677 publication(s) have been published within this topic receiving 1723143 citation(s).

...read more

Papers
  More

Journal ArticleDOI: 10.1145/1961189.1961199
Chih-Chung Chang1, Chih-Jen Lin1Institutions (1)
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

37,868 Citations


Open accessBook
Stephen Boyd1, Neal Parikh1, Eric Chu1, Borja Peleato1  +1 moreInstitutions (2)
23 May 2011-
Abstract: Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.

...read more

Topics: Online machine learning (59%), Statistical learning theory (57%), Convex optimization (56%) ...read more

14,958 Citations


Journal ArticleDOI: 10.1023/A:1009715923555
Christopher John Burges1Institutions (1)
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

...read more

14,909 Citations


Open accessBook
Nello Cristianini1, John Shawe-Taylor2Institutions (2)
01 Jan 2000-
Abstract: From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.

...read more

13,269 Citations


Open accessJournal ArticleDOI: 10.1109/TPAMI.2009.167
Abstract: We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.

...read more

  • Fig. 1. Detections obtained with a single component person model. The model is defined by a coarse root filter (a), several higher resolution part filters (b) and a spatial model for the location of each part relative to the root (c). The filters specify weights for histogram of oriented gradients features. Their visualization show the positive weights at different orientations. The visualization of the spatial models reflects the “cost” of placing the center of a part at different locations relative to the root.
    Fig. 1. Detections obtained with a single component person model. The model is defined by a coarse root filter (a), several higher resolution part filters (b) and a spatial model for the location of each part relative to the root (c). The filters specify weights for histogram of oriented gradients features. Their visualization show the positive weights at different orientations. The visualization of the spatial models reflects the “cost” of placing the center of a part at different locations relative to the root.
  • Fig. 2. Detections obtained with a 2 component bicycle model. These examples illustrate the importance of deformations mixture models. In this model the first component captures sideways views of bicycles while the second component captures frontal and near frontal views. The sideways component can deform to match a “wheelie”.
    Fig. 2. Detections obtained with a 2 component bicycle model. These examples illustrate the importance of deformations mixture models. In this model the first component captures sideways views of bicycles while the second component captures frontal and near frontal views. The sideways component can deform to match a “wheelie”.
  • Fig. 4. The matching process at one scale. Responses from the root and part filters are computed a different resolutions in the feature pyramid. The transformed responses are combined to yield a final score for each root location. We show the responses and transformed responses for the “head” and “right shoulder” parts. Note how the “head” filter is more discriminative. The combined scores clearly show two good hypothesis for the object at this scale.
    Fig. 4. The matching process at one scale. Responses from the root and part filters are computed a different resolutions in the feature pyramid. The transformed responses are combined to yield a final score for each root location. We show the responses and transformed responses for the “head” and “right shoulder” parts. Note how the “head” filter is more discriminative. The combined scores clearly show two good hypothesis for the object at this scale.
  • Fig. 5. (a) and (b) are the initial root filters for a car model (the result of Phase 1 of the initialization process). (c) is the initial part-based model for a car (the result of Phase 3 of the initialization process).
    Fig. 5. (a) and (b) are the initial root filters for a car model (the result of Phase 1 of the initialization process). (c) is the initial part-based model for a car (the result of Phase 3 of the initialization process).
  • Fig. 6. PCA of HOG features. Each eigenvector is displayed as a 4 by 9 matrix so that each row corresponds to one normalization factor and each column to one orientation bin. The eigenvalues are displayed on top of the eigenvectors. The linear subspace spanned by the top 11 eigenvectors captures essentially all of the information in a feature vector. Note how all of the top eigenvectors are either constant along each column or row of the matrix representation.
    Fig. 6. PCA of HOG features. Each eigenvector is displayed as a 4 by 9 matrix so that each row corresponds to one normalization factor and each column to one orientation bin. The eigenvalues are displayed on top of the eigenvectors. The linear subspace spanned by the top 11 eigenvectors captures essentially all of the information in a feature vector. Note how all of the top eigenvectors are either constant along each column or row of the matrix representation.
  • + 7

9,553 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2022173
20215,096
20205,607
20195,834
20185,355
20174,881

Top Attributes

Show by:

Topic's top 5 most impactful authors

Johan A. K. Suykens

175 papers, 21.2K citations

Bernhard Schölkopf

82 papers, 42.4K citations

Yingjie Tian

78 papers, 1.9K citations

Jon Atli Benediktsson

67 papers, 6.2K citations

Lorenzo Bruzzone

60 papers, 8.5K citations

Network Information
Related Topics (5)
Feature vector

48.8K papers, 954.4K citations

96% related
Feature selection

41.4K papers, 1M citations

96% related
Artificial neural network

207K papers, 4.5M citations

95% related
Feature extraction

111.8K papers, 2.1M citations

95% related
Dimensionality reduction

21.9K papers, 579.2K citations

94% related