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    NIPS
    2007
    116views Information Technology» more  NIPS 2007»
    Learning with Transformation Invariant Kernels
    14 years 2 months ago
    Learning with Transformation Invariant Kernels
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    This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation inv...
    Christian Walder, Olivier Chapelle
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    NIPS
    2007
    143views Information Technology» more  NIPS 2007»
    Near-Maximum Entropy Models for Binary Neural Representations of Natural Images
    14 years 2 months ago
    Near-Maximum Entropy Models for Binary Neural Representations of Natural Images
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    Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer f...
    Matthias Bethge, Philipp Berens
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    NIPS
    2007
    122views Information Technology» more  NIPS 2007»
    Second Order Bilinear Discriminant Analysis for single trial EEG analysis
    14 years 2 months ago
    Second Order Bilinear Discriminant Analysis for single trial EEG analysis
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    Traditional analysis methods for single-trial classification of electroencephalography (EEG) focus on two types of paradigms: phase locked methods, in which the amplitude of the ...
    Christoforos Christoforou, Paul Sajda, Lucas C. Pa...
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    NIPS
    2007
    154views Information Technology» more  NIPS 2007»
    Automatic Generation of Social Tags for Music Recommendation
    14 years 2 months ago
    Automatic Generation of Social Tags for Music Recommendation
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    Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web2.0” recommender ...
    Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, ...
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    NIPS
    2007
    142views Information Technology» more  NIPS 2007»
    Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion
    14 years 2 months ago
    Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion
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    We consider apprenticeship learning—learning from expert demonstrations—in the setting of large, complex domains. Past work in apprenticeship learning requires that the expert...
    J. Zico Kolter, Pieter Abbeel, Andrew Y. Ng
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    NIPS
    2007
    146views Information Technology» more  NIPS 2007»
    Anytime Induction of Cost-sensitive Trees
    14 years 2 months ago
    Anytime Induction of Cost-sensitive Trees
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    Machine learning techniques are increasingly being used to produce a wide-range of classifiers for complex real-world applications that involve nonuniform testing costs and miscl...
    Saher Esmeir, Shaul Markovitch
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    NIPS
    2007
    137views Information Technology» more  NIPS 2007»
    Privacy-Preserving Belief Propagation and Sampling
    14 years 2 months ago
    Privacy-Preserving Belief Propagation and Sampling
    Download  www.cis.upenn.edu
    We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other local algorithms — distributed multiparty protocols in which each party or verte...
    Michael Kearns, Jinsong Tan, Jennifer Wortman
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    NIPS
    2007
    217views Information Technology» more  NIPS 2007»
    Semi-Supervised Multitask Learning
    14 years 2 months ago
    Semi-Supervised Multitask Learning
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    A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M partially labeled data mani...
    Qiuhua Liu, Xuejun Liao, Lawrence Carin
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    NIPS
    2007
    155views Information Technology» more  NIPS 2007»
    Boosting Algorithms for Maximizing the Soft Margin
    14 years 2 months ago
    Boosting Algorithms for Maximizing the Soft Margin
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    We present a novel boosting algorithm, called SoftBoost, designed for sets of binary labeled examples that are not necessarily separable by convex combinations of base hypotheses....
    Manfred K. Warmuth, Karen A. Glocer, Gunnar Rä...
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    NIPS
    2007
    131views Information Technology» more  NIPS 2007»
    Parallelizing Support Vector Machines on Distributed Computers
    14 years 2 months ago
    Parallelizing Support Vector Machines on Distributed Computers
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    Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel ...
    Edward Y. Chang, Kaihua Zhu, Hao Wang, Hongjie Bai...
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