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    ML
    2002
    ACM
    146views Machine Learning» more  ML 2002»
    Kernel Matching Pursuit
    13 years 4 months ago
    Kernel Matching Pursuit
    Download  www.iro.umontreal.ca
    Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
    Pascal Vincent, Yoshua Bengio
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    ML
    2002
    ACM
    168views Machine Learning» more  ML 2002»
    On Average Versus Discounted Reward Temporal-Difference Learning
    13 years 4 months ago
    On Average Versus Discounted Reward Temporal-Difference Learning
    Download  web.mit.edu
    We provide an analytical comparison between discounted and average reward temporal-difference (TD) learning with linearly parameterized approximations. We first consider the asympt...
    John N. Tsitsiklis, Benjamin Van Roy
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    ML
    2002
    ACM
    178views Machine Learning» more  ML 2002»
    Metric-Based Methods for Adaptive Model Selection and Regularization
    13 years 4 months ago
    Metric-Based Methods for Adaptive Model Selection and Regularization
    Download  www.cs.cmu.edu
    We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
    Dale Schuurmans, Finnegan Southey
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    ML
    2002
    ACM
    220views Machine Learning» more  ML 2002»
    Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
    13 years 4 months ago
    Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
    Download  www.mth.kcl.ac.uk
    I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
    Peter Sollich
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    ML
    2002
    ACM
    127views Machine Learning» more  ML 2002»
    Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
    13 years 4 months ago
    Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
    Download  www.stat.purdue.edu
    We examine methods for constructing regression ensembles based on a linear program (LP). The ensemble regression function consists of linear combinations of base hypotheses generat...
    Gunnar Rätsch, Ayhan Demiriz, Kristin P. Benn...
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    ML
    2002
    ACM
    246views Machine Learning» more  ML 2002»
    Bayesian Clustering by Dynamics
    13 years 4 months ago
    Bayesian Clustering by Dynamics
    Download  www.cs.arizona.edu
    This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to disc...
    Marco Ramoni, Paola Sebastiani, Paul R. Cohen
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    ML
    2002
    ACM
    114views Machine Learning» more  ML 2002»
    Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
    13 years 4 months ago
    Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
    Download  www.cs.ou.edu
    The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers...
    Amy McGovern, J. Eliot B. Moss, Andrew G. Barto
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    ML
    2002
    ACM
    123views Machine Learning» more  ML 2002»
    Feature Generation Using General Constructor Functions
    13 years 4 months ago
    Feature Generation Using General Constructor Functions
    Download  www.cs.technion.ac.il
    Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creatin...
    Shaul Markovitch, Dan Rosenstein
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    ML
    2002
    ACM
    141views Machine Learning» more  ML 2002»
    On the Existence of Linear Weak Learners and Applications to Boosting
    13 years 4 months ago
    On the Existence of Linear Weak Learners and Applications to Boosting
    Download  www.ece.mcgill.ca
    We consider the existence of a linear weak learner for boosting algorithms. A weak learner for binary classification problems is required to achieve a weighted empirical error on t...
    Shie Mannor, Ron Meir
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    ML
    2002
    ACM
    146views Machine Learning» more  ML 2002»
    Variable Resolution Discretization in Optimal Control
    13 years 4 months ago
    Variable Resolution Discretization in Optimal Control
    Download  www.ri.cmu.edu
    Abstract. The problemof state abstractionis of centralimportancein optimalcontrol,reinforcement learning and Markov decision processes. This paper studies the case of variable reso...
    Rémi Munos, Andrew W. Moore
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