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    ML
    2002
    ACM
    106views Machine Learning» more  ML 2002»
    Statistical Properties and Adaptive Tuning of Support Vector Machines
    14 years 3 days ago
    Statistical Properties and Adaptive Tuning of Support Vector Machines
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    Yi Lin, Grace Wahba, Hao Zhang, Yoonkyung Lee
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    ML
    2002
    ACM
    223views Machine Learning» more  ML 2002»
    Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?
    14 years 3 days ago
    Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?
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    The choice of the kernel function is crucial to most applications of support vector machines. In this paper, however, we show that in the case of text classification, term-frequenc...
    Edda Leopold, Jörg Kindermann
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    ML
    2002
    ACM
    142views Machine Learning» more  ML 2002»
    Convergence of a Generalized SMO Algorithm for SVM Classifier Design
    14 years 3 days ago
    Convergence of a Generalized SMO Algorithm for SVM Classifier Design
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    S. Sathiya Keerthi, Elmer G. Gilbert
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    ML
    2002
    ACM
    121views Machine Learning» more  ML 2002»
    Near-Optimal Reinforcement Learning in Polynomial Time
    14 years 3 days ago
    Near-Optimal Reinforcement Learning in Polynomial Time
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    We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
    Michael J. Kearns, Satinder P. Singh
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    ML
    2002
    ACM
    143views Machine Learning» more  ML 2002»
    A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
    14 years 3 days ago
    A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
    Download  www.cis.upenn.edu
    An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
    Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
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    ML
    2002
    ACM
    104views Machine Learning» more  ML 2002»
    A Simple Decomposition Method for Support Vector Machines
    14 years 3 days ago
    A Simple Decomposition Method for Support Vector Machines
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    The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this pape...
    Chih-Wei Hsu, Chih-Jen Lin
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    ML
    2002
    ACM
    180views Machine Learning» more  ML 2002»
    Gene Selection for Cancer Classification using Support Vector Machines
    14 years 3 days ago
    Gene Selection for Cancer Classification using Support Vector Machines
    Download  www.kyb.tuebingen.mpg.de
    Isabelle Guyon, Jason Weston, Stephen Barnhill, Vl...
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    ML
    2002
    ACM
    163views Machine Learning» more  ML 2002»
    Structural Modelling with Sparse Kernels
    14 years 3 days ago
    Structural Modelling with Sparse Kernels
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    A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
    Steve R. Gunn, Jaz S. Kandola
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    ML
    2002
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    145views Machine Learning» more  ML 2002»
    Boosting Methods for Regression
    14 years 3 days ago
    Boosting Methods for Regression
    Download  sci2s.ugr.es
    In this paper we examine ensemble methods for regression that leverage or "boost" base regressors by iteratively calling them on modified samples. The most successful lev...
    Nigel Duffy, David P. Helmbold
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    ML
    2002
    ACM
    128views Machine Learning» more  ML 2002»
    A Simple Method for Generating Additive Clustering Models with Limited Complexity
    14 years 3 days ago
    A Simple Method for Generating Additive Clustering Models with Limited Complexity
    Download  www.socsci.uci.edu
    Additive clustering was originally developed within cognitive psychology to enable the development of featural models of human mental representation. The representational flexibili...
    Michael D. Lee
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