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Support Vector Machine Support Vector machine is a training algorithm {we have learned nearest neighbor, identification tree, genetic algorithm and neural net} for learning classification rules (discrete classes for each input instance) and regression functions (continuous output for every input). Support vectors refer to the vectors with non-zero Lagrangian multiplier (vectors that contribute to the decision boundary). Vectors that contribute to the street are called support vectors, and hence the algorithm is called support vector machines (finding the support vectors and using them to determine how unknown data could be classified). web.mit.edu /chungc/6.034/l10.html   (0 words)

SVM - Support Vector Machines Using a kernel function, SVM’s are an alternative training method for polynomial, radial basis function and multi-layer perceptron classifiers in which the weights of the network are found by solving a quadratic programming problem with linear constraints, rather than by solving a non-convex, unconstrained minimization problem as in standard neural network training. So the goal of SVM modeling is to find the optimal hyperplane that separates clusters of vector in such a way that cases with one category of the target variable are on one side of the plane and cases with the other category are on the other size of the plane. The vectors near the hyperplane are the support vectors. www.dtreg.com /svm.htm   (0 words)

Support Vector Machine Regression Support Vector Machines are very specific class of algorithms, characterized by usage of kernels, absence of local minima, sparseness of the solution and capacity control obtained by acting on the margin, or on number of support vectors, etc. Support Vector Machine can be applied not only to classification problems but also to the case of regression. One of the advantages of Support Vector Machine, and Support Vector Regression as the part of it, is that it can be used to avoid difficulties of using linear functions in the high dimensional feature space and optimization problem is transformed into dual convex quadratic programmes. members.tripod.com /kernelsvm   (0 words)

Vector Machine Since a PMML document may contain some SVM models, for instance for multiclass problems or for trees with SVM nodes, which often share common support vectors, it is useful to store the SVs only in one place of the PMML document. The attribute svmRepresentation defines whether the SVM function is defined via support vectors or via the coefficients of the hyperplane for the case of linear kernel functions. The elements VectorInstance represent support vectors and are referenced by the id-attribute. www.dmg.org /v3-0/SupportVectorMachine.html   (0 words)

Support vector machine - Wikipedia, the free encyclopedia Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. As we are interested in the maximum margin, we are interested in the support vectors and the parallel hyperplanes (to the optimal hyperplane) closest to these support vectors in either class. A version of a SVM for regression was proposed in 1996 by Vapnik, Harris Drucker, Chris Burges, Linda Kaufman and Alex Smola. en.wikipedia.org /wiki/Support_vector_machine   (1600 words)

Support Vector Learning   (Site not responding. Last check: 2007-10-09) The Support Vector Machine is a new type of learning machine for pattern recognition and regression problems which constructs its solution in terms of a subset of the training data, the Support Vectors. Thus there is reason to believe that decision rules constructed by the support vector algorithm do not reflect incapabilities of the learning machine (as in the case of an overfitted artificial neural network) but rather regularities of the data. We have applied support vector machines to object recognition (Blanz et al., 1996); for benchmarking, our set of images of rendered chair models is available on our ftp server. www.kyb.tuebingen.mpg.de /bu/people/bs/svm.html   (0 words)

Gist SVM Server The SVM algorithm operates by mapping the given training set into a possibly high-dimensional feature space and attempting to locate in that space a plane that separates the positive from the negative examples. Completely specifying a support vector machine therefore requires specifying two parameters: the kernel function and the magnitude of the penalty for violating the soft margin. In the SVM server, s is usually set equal to the median of the Euclidean distances from each positive example to the nearest negative example. svm.sdsc.edu /svm-overview.html   (0 words)

Support Vector Machines Support Vector Machines were invented by Vladimir Vapnik. The output of an SVM is an uncalibrated value, not a posterior probability of a class given an input. Platt, Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods (84K gzipped PS file), Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Scholkopf, D. research.microsoft.com /~jplatt/svm.html   (0 words)

Support Vector Machines Support vector machines (SVMs) and other supervised learning techniques such as Parzen windows, Fisher's linear discriminant, and decision tree learners, use a training set to specify in advance which data should cluster together. As applied to gene expression data, an SVM would begin with a set of genes that have a common function, for example, genes coding for ribosomal proteins or genes coding for components of the proteasome. SVMs are now being employed for a wide variety of applications, a list of which can be seen here. www.cse.ucsc.edu /research/compbio/genex/svm.html   (0 words)

SVM Application List Support vector machines are based on statistical learning theory and found to work well in comparison to neural networks in several other applications. Support Vector Regression used to predict the temperature of a cable buried underground, based on weather data from the previous 24 hours. Mattera et al report that SVM are effective for such tasks and that their main advantage is the possibility of trading off the required accuracy with the number of Support Vectors. www.clopinet.com /isabelle/Projects/SVM/applist.html   (0 words)

Support vector machines Support vector machines map a given set of binary labeled training data to a high-dimensional feature space and separate the two classes of data with a maximum margin hyperplane. These points are called support vectors and are the points that lie closest to the separating hyperplane. The use of a kernel function allows the support vector machine to operate efficiently in a nonlinear high-dimensional feature spaces without being adversely affected by the dimensionality of that space. www.cse.ucsc.edu /research/compbio/genex/genexTR2html/node17.html   (0 words)

Software   (Site not responding. Last check: 2007-10-09) SVM implementation to be run inside a database. Convext QP solver for large-scale support vector machines classification. SVM program for running under Windows.It uses SMO algorithm, so it is very fast and easy to use. www.kernel-machines.org /software.html   (0 words)

SVM - Support Vector Machines Gist is a C implementation of support vector machine classification and kernel principal components analysis. The SVM part of Gist is available as an interactive web server at http://svm.sdsc.edu and it is a very convenient option for users that want to experiment with small datasets (several hundreds patterns). PSVM (Proximal Support Vector Machine) is a MATLAB script by Fung and Mangasarian which classifies patterns by assigning them to the closest of two parallel planes. www.support-vector-machines.org /SVM_soft.html   (0 words)

Support vector machine and kernel methods starter Support vector machines and kernel methods have proven as powerful tools in numerous applications and have thus gained widespread popularity e.g. Support Vector Clustering by A. Ben-Hur et al. Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds by J. Fernández Pierna et al. www.models.kvl.dk /research/svm_starter/index.asp   (0 words)

Support Vector Machines - The Book -  Support Vector This book 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, bioinformatics. Support Vector training algorithms are described in detail, with pseudocode, as well as principles of optimisation, generalisation and kernel theory. www.support-vector.net   (0 words)

Support Vector Machine - The Software SVM and KM matlab toolbox, from Insa de Rouen Gist: software for support vector machine classification and for kernel principal components analysis. MATLAB Support Vector Machine Toolbox by Gavin Cawley www.support-vector.net /software.html   (0 words)

SVM-Light Support Vector Machine is an implementation of Vapnik's Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. The equivalent of training error for a ranking SVM is the number of training pairs that are misordered by the learned model. VCdim is now estimated based on the radius of the support vectors. www.cs.cornell.edu /People/tj/svm_light   (0 words)

SUPPORT VECTOR MACHINE IN CHEMISTRY In recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance. SVM is fast becoming a powerful tool of chemometrics. www.worldscibooks.com /chemistry/5589.html   (0 words)

SVM Page of Keerthi's Group at NUS, Singapore   (Site not responding. Last check: 2007-10-09) In very simple terms an SVM corresponds to a linear method in a very high dimensional feature space that is nonlinearly related to the input space. One of the key theoretical results of this paper is that, as sigma goes to infinity the SVM classifier with gaussian kernel tends to the linear SVM classifier. This is an important result since it means that, a gaussian SVM designed to minimize the generalization error over all possible C and sigma has to be equal or better in performance than a linear SVM classifier with C tuned. guppy.mpe.nus.edu.sg /~mpessk/svm.shtml   (0 words)

The Formulation of Support Vector Machine   (Site not responding. Last check: 2007-10-09) In this chapter the idea of using Support Vector Machines in pattern classification is presented. The formulation of SVM is constructed starting from a simple linear maximum margin classifier. Finally the claim that SVM training achieves the lowest necessary capacity for a given classification task will be investigated. svr-www.eng.cam.ac.uk /~kkc21/thesis_main/node8.html   (0 words)

LEAST SQUARES SUPPORT VECTOR MACHINES This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. www.worldscibooks.com /compsci/5089.html   (0 words)

Support Vector Machine Theory and Applications We would like to invite you to submit an extended abstract or a paper and participate in a special workshop on "Support Vector Machine (SVM) theory and applications" to be held in Chania, Crete in Greece at the Advanced Course on Artificial Intelligence 1999 (ACAI '99) on July 14, 1999. SVMs have increased in popularity over the past few years due to their solid theoretical foundations and their state-of-the-art performance in a number of applications. Send a copy of the extended abstract or paper by March 15, 1999 electronically (.ps or.pdf files) to acai99@ai.mit.edu. cbcl.mit.edu /cbcl/acai99/acai99.html   (0 words)

MATLAB Support Vector Machine Toolbox (via CobWeb/3.1 planetlab2.cs.unc.edu)   (Site not responding. Last check: 2007-10-09) For a good introduction to support vector machines, see the excellent book by Cristianini and Shawe-Taylor [3]. Support for multi-class support vector classification using max wins, pairwise [4] and DAG-SVM [5] algorithms. Support for multi-class pattern recognition using maxwins, pairwise [4] and DAG-SVM [5] algorithms. theoval.sys.uea.ac.uk.cob-web.org:8888 /~gcc/svm/toolbox   (0 words)

Automated Text Categorization Using Support Vector Machine - Kwok (ResearchIndex)   (Site not responding. Last check: 2007-10-09) Abstract: In this paper, we study the use of support vector machine in text categorization. Thus, SVM adapts efficiently in dynamic environments that require frequent additions to the document collection. 66 Comparing support vector machines with Gaussian kernels to r.. citeseer.ist.psu.edu /kwok98automated.html   (0 words)

SVM - Support Vector Machines The optimum separation hyperplane (OSH) is the linear classifier with the maximum margin for a given finite set of learning patterns. The OSH computation with a linear support vector machine is presented in this section. The "SVM - Support Vector Machines" Portal is part of the OIRI network www.support-vector-machines.org /SVM_osh.html   (0 words)

SVMTool The SVMTool is a simple and effective generator of sequential taggers based on Support Vector Machines. Given a training set of examples (either annotated or unannotated), it is responsible for the training of a set of SVM classifiers. Given a text corpus (one token per line) and the path to a previously learned SVM model, it performs the sequential tagging of a sequence of words. www.lsi.upc.edu /~nlp/SVMTool   (0 words)

Nonlinear Support Vector Machine So far the SVM classifier can only have a linear hyper-plane as its decision surface. The motivation for this extension is that a SVM with nonlinear decision surface can classify nonlinearly separable data. It is interesting to note that by choosing different kernel functions, the SVM can emulate some well known classifiers [Osuna et al., 1997b], as shown in table svr-www.eng.cam.ac.uk /~kkc21/thesis_main/node14.html   (0 words)

LIBSVM -- A Library for Support Vector Machines (via CobWeb/3.1 planetlab2.cs.unc.edu)   (Site not responding. Last check: 2007-10-09) The technique used is the support vector regression. LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). Acknowledgments: This work was supported in part by the National Science Council of Taiwan via the grant NSC 89-2213-E-002-106. www.csie.ntu.edu.tw.cob-web.org:8888 /~cjlin/libsvm   (0 words)

Support Vector Machine This new version also includes a new algorithm for training large-scale transductive SVMs. The software must not be modified and distributed without prior permission of the author. European Conference on Machine Learning (ECML), Claire Nédellec and Céline Rouveirol (ed.), 1998. homepages.cae.wisc.edu /~ece539/software/svm_light   (0 words)

Support Vector Machine VectorFields defines which entries in the vectors correspond to which fields. The sizes of the sparse arrays must match the number of fields included in the VectorFields element. In the same way, the scoring of the other support vectors delivers www.dmg.org /v3-1/SupportVectorMachine.html   (0 words)

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