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    NCA
    2008
    IEEE
    106views Computer Networks» more  NCA 2008»
    Polynomial kernel adaptation and extensions to the SVM classifier learning
    13 years 11 months ago
    Polynomial kernel adaptation and extensions to the SVM classifier learning
    Download  www.mech.eng.unimelb.edu.au
    Three extensions to the Kernel-AdaTron training algorithm for Support Vector Machine classifier learning are presented. These extensions allow the trained classifier to adhere more...
    Ramy Saad, Saman K. Halgamuge, Jason Li
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    JMLR
    2006
    150views more  JMLR 2006»
    Building Support Vector Machines with Reduced Classifier Complexity
    13 years 11 months ago
    Building Support Vector Machines with Reduced Classifier Complexity
    Download  jmlr.csail.mit.edu
    Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overc...
    S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCos...
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    JMLR
    2006
    105views more  JMLR 2006»
    Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
    13 years 11 months ago
    Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
    Download  eprints.pascal-network.org
    Parallel software for solving the quadratic program arising in training support vector machines for classification problems is introduced. The software implements an iterative dec...
    Luca Zanni, Thomas Serafini, Gaetano Zanghirati
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    JMLR
    2006
    186views more  JMLR 2006»
    Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
    13 years 11 months ago
    Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
    Download  people.cs.uchicago.edu
    We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
    Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
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    JMLR
    2006
    131views more  JMLR 2006»
    On Representing and Generating Kernels by Fuzzy Equivalence Relations
    13 years 11 months ago
    On Representing and Generating Kernels by Fuzzy Equivalence Relations
    Download  jmlr.csail.mit.edu
    Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...
    Bernhard Moser
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    KES
    2008
    Springer
    191views Information Technology» more  KES 2008»
    Classification and Retrieval through Semantic Kernels
    13 years 11 months ago
    Classification and Retrieval through Semantic Kernels
    Download  www.di.uniba.it
    Abstract. This work proposes a family of language-independent semantic kernel functions defined for individuals in an ontology. This allows exploiting wellfounded kernel methods fo...
    Claudia d'Amato, Nicola Fanizzi, Floriana Esposito
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    JCP
    2006
    102views more  JCP 2006»
    Efficient Formulations for 1-SVM and their Application to Recommendation Tasks
    13 years 11 months ago
    Efficient Formulations for 1-SVM and their Application to Recommendation Tasks
    Download  www.academypublisher.com
    The present paper proposes new approaches for recommendation tasks based on one-class support vector machines (1-SVMs) with graph kernels generated from a Laplacian matrix. We intr...
    Yasutoshi Yajima, Tien-Fang Kuo
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    JAIR
    2006
    113views more  JAIR 2006»
    Generative Prior Knowledge for Discriminative Classification
    13 years 11 months ago
    Generative Prior Knowledge for Discriminative Classification
    Download  www.jair.org
    We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs...
    Arkady Epshteyn, Gerald DeJong
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    JMLR
    2008
    140views more  JMLR 2008»
    Aggregation of SVM Classifiers Using Sobolev Spaces
    13 years 11 months ago
    Aggregation of SVM Classifiers Using Sobolev Spaces
    Download  www.cmi.univ-mrs.fr
    This paper investigates statistical performances of Support Vector Machines (SVM) and considers the problem of adaptation to the margin parameter and to complexity. In particular ...
    Sébastien Loustau
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    JMLR
    2008
    123views more  JMLR 2008»
    Optimization Techniques for Semi-Supervised Support Vector Machines
    13 years 11 months ago
    Optimization Techniques for Semi-Supervised Support Vector Machines
    Download  jmlr.csail.mit.edu
    Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs...
    Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keer...
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