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    JMLR
    2006
    145views more  JMLR 2006»
    Ensemble Pruning Via Semi-definite Programming
    13 years 4 months ago
    Ensemble Pruning Via Semi-definite Programming
    Download  dollar.biz.uiowa.edu
    An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead t...
    Yi Zhang 0006, Samuel Burer, W. Nick Street
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    JMLR
    2006
    143views more  JMLR 2006»
    Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
    13 years 4 months ago
    Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
    Download  www.aaai.org
    We study a sequential variance reduction technique for Monte Carlo estimation of functionals in Markov Chains. The method is based on designing sequential control variates using s...
    Rémi Munos
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    JMLR
    2006
    108views more  JMLR 2006»
    Learning Spectral Clustering, With Application To Speech Separation
    13 years 4 months ago
    Learning Spectral Clustering, With Application To Speech Separation
    Download  www.cs.berkeley.edu
    Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same clu...
    Francis R. Bach, Michael I. Jordan
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    JMLR
    2006
    99views more  JMLR 2006»
    Worst-Case Analysis of Selective Sampling for Linear Classification
    13 years 4 months ago
    Worst-Case Analysis of Selective Sampling for Linear Classification
    Download  jmlr.csail.mit.edu
    A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
    Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
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    JMLR
    2006
    90views more  JMLR 2006»
    Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
    13 years 4 months ago
    Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
    Download  www.cs.technion.ac.il
    We present worst case bounds for the learning rate of a known prediction method that is based on hierarchical applications of binary context tree weighting (CTW) predictors. A heu...
    Ron Begleiter, Ran El-Yaniv
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    JMLR
    2006
    103views more  JMLR 2006»
    On Model Selection Consistency of Lasso
    13 years 4 months ago
    On Model Selection Consistency of Lasso
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    Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such...
    Peng Zhao, Bin Yu
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    JMLR
    2006
    116views more  JMLR 2006»
    Step Size Adaptation in Reproducing Kernel Hilbert Space
    13 years 4 months ago
    Step Size Adaptation in Reproducing Kernel Hilbert Space
    Download  jmlr.csail.mit.edu
    This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online lear...
    S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex ...
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    JMLR
    2006
    143views more  JMLR 2006»
    Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
    13 years 4 months ago
    Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
    Download  jmlr.csail.mit.edu
    We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function i...
    Régis Vert, Jean-Philippe Vert
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    JMLR
    2006
    140views more  JMLR 2006»
    Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
    13 years 4 months ago
    Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
    Download  jmlr.csail.mit.edu
    The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
    Masashi Sugiyama
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    JMLR
    2006
    105views more  JMLR 2006»
    Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
    13 years 4 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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