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468
The analysis of decomposition methods for support vector machines
 IEEE Transactions on Neural Networks
, 1999
"... Abstract. The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for boundconstrained SVM formulations we demonstrate that the w ..."
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Cited by 134 (21 self)
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Abstract. The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for boundconstrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.
Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites
, 2000
"... Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as a classification pro ..."
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Cited by 133 (14 self)
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Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines (SVMs) for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.
Dimensionality Reduction via Sparse Support Vector Machines
 Journal of Machine Learning Research
, 2003
"... We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to prod ..."
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Cited by 121 (14 self)
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We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to produce a final nonlinear model. The method exploits the fact that a linear SVM (no kernels) with # 1 norm regularization inherently performs variable selection as a sidee#ect of minimizing capacity of the SVM model. The distribution of the linear model weights provides a mechanism for ranking and interpreting the e#ects of variables.
Support Vector Machines: Hype or Hallelujah?
 SIGKDD Explorations
, 2003
"... Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective. The classification ..."
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Cited by 119 (1 self)
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Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective. The classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective. While this overview is not comprehensive, it does provide resources for those interested in further exploring SVMs.
Learning the Kernel with Hyperkernels
, 2003
"... This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical es ..."
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Cited by 115 (2 self)
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This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem very much akin to the problem of minimizing a regularized risk functional. We state the
Incremental algorithms for hierarchical classification
 Journal of Machine Learning Research
, 2004
"... We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce a new algorithm that incrementally learns a linearthreshold classifier for each node of the taxonomy. A hierarchical classification is obtained b ..."
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Cited by 109 (9 self)
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We study the problem of classifying data in a given taxonomy when classifications associated with multiple and/or partial paths are allowed. We introduce a new algorithm that incrementally learns a linearthreshold classifier for each node of the taxonomy. A hierarchical classification is obtained by evaluating the trained node classifiers in a topdown fashion. To evaluate classifiers in our multipath framework, we define a new hierarchical loss function, the Hloss, capturing the intuition that whenever a classification mistake is made on a node of the taxonomy, then no loss should be charged for any additional mistake occurring in the subtree of that node. Making no assumptions on the mechanism generating the data instances, and assuming a linear noise model for the labels, we bound the Hloss of our online algorithm in terms of the Hloss of a reference classifier knowing the true parameters of the labelgenerating process. We show that, in expectation, the excess cumulative Hloss grows at most logarithmically in the length of the data sequence. Furthermore, our analysis reveals the precise dependence of the rate of convergence on the eigenstructure of the data each node observes. Our theoretical results are complemented by a number of experiments on texual corpora. In these experiments we show that, after only one epoch of training, our algorithm performs much better than Perceptronbased hierarchical classifiers, and reasonably close to a hierarchical support vector machine.
Ranking with large margin principle: Two approaches
 In Proceedings of Advances in Neural Information Processing Systems
, 2002
"... We discuss the problem of ranking instances with the use of a “large margin ” principle. We introduce two main approaches: the first is the “fixed margin ” policy in which the margin of the closest neighboring classes is being maximized — which turns out to be a direct generalization of SVM to ranki ..."
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Cited by 95 (0 self)
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We discuss the problem of ranking instances with the use of a “large margin ” principle. We introduce two main approaches: the first is the “fixed margin ” policy in which the margin of the closest neighboring classes is being maximized — which turns out to be a direct generalization of SVM to ranking learning. The second approach allows for different margins where the sum of margins is maximized. This approach is shown to reduce toSVM when the number of classes. Both approaches are optimal in size of where is the total number of training examples. Experiments performed on visual classification and “collaborative filtering ” show that both approaches outperform existing ordinal regression algorithms applied for ranking and multiclass SVM applied to general multiclass classification. 1
A Review of Kernel Methods in Machine Learning
, 2006
"... We review recent methods for learning with positive definite kernels. All these methods formulate learning and estimation problems as linear tasks in a reproducing kernel Hilbert space (RKHS) associated with a kernel. We cover a wide range of methods, ranging from simple classifiers to sophisticate ..."
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Cited by 95 (4 self)
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We review recent methods for learning with positive definite kernels. All these methods formulate learning and estimation problems as linear tasks in a reproducing kernel Hilbert space (RKHS) associated with a kernel. We cover a wide range of methods, ranging from simple classifiers to sophisticated methods for estimation with structured data.
Feature space interpretation of svms with indefinite kernels
 IEEE Trans Pattern Anal Mach Intell
, 2005
"... Abstract—Kernel methods are becoming increasingly popular for various kinds of machine learning tasks, the most famous being the support vector machine (SVM) for classification. The SVM is well understood when using conditionally positive definite (cpd) kernel functions. However, in practice, noncp ..."
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Cited by 92 (3 self)
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Abstract—Kernel methods are becoming increasingly popular for various kinds of machine learning tasks, the most famous being the support vector machine (SVM) for classification. The SVM is well understood when using conditionally positive definite (cpd) kernel functions. However, in practice, noncpd kernels arise and demand application in SVMs. The procedure of “plugging ” these indefinite kernels in SVMs often yields good empirical classification results. However, they are hard to interpret due to missing geometrical and theoretical understanding. In this paper, we provide a step toward the comprehension of SVM classifiers in these situations. We give a geometric interpretation of SVMs with indefinite kernel functions. We show that such SVMs are optimal hyperplane classifiers not by margin maximization, but by minimization of distances between convex hulls in pseudoEuclidean spaces. By this, we obtain a sound framework and motivation for indefinite SVMs. This interpretation is the basis for further theoretical analysis, e.g., investigating uniqueness, and for the derivation of practical guidelines like characterizing the suitability of indefinite SVMs. Index Terms—Support vector machine, indefinite kernel, pseudoEuclidean space, separation of convex hulls, pattern recognition. æ 1
kernlab – An S4 Package for Kernel Methods in R
 Journal of Statistical Software
, 2004
"... kernlab is an extensible package for kernelbased machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernelbased algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and ..."
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Cited by 89 (2 self)
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kernlab is an extensible package for kernelbased machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernelbased algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, kernel feature analysis, online kernel methods and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method.