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    AAAI
    2008
    195views Intelligent Agents» more  AAAI 2008»
    Transfer Learning via Dimensionality Reduction
    13 years 11 months ago
    Transfer Learning via Dimensionality Reduction
    Download  www.cs.ust.hk
    Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...
    Sinno Jialin Pan, James T. Kwok, Qiang Yang
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    AAI
    2005
    113views more  AAI 2005»
    Analysis and Synthesis of Learning Agent's Communicative Behavior
    13 years 8 months ago
    Analysis and Synthesis of Learning Agent's Communicative Behavior
    Download  honiden-lab.ex.nii.ac.jp
    This paper is about people. It is about understanding how learning and communication mutually influence one another; allowing people to infer each other's communicative behavi...
    Nik Nailah Binti Abdullah, Stefano A. Cerri
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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 8 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
    2006
    ACM
    142views Machine Learning» more  ML 2006»
    The max-min hill-climbing Bayesian network structure learning algorithm
    13 years 8 months ago
    The max-min hill-climbing Bayesian network structure learning algorithm
    Download  www.dsl-lab.org
    We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
    Ioannis Tsamardinos, Laura E. Brown, Constantin F....
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    ICML
    2007
    IEEE
    172views Machine Learning» more  ICML 2007»
    Conditional random fields for multi-agent reinforcement learning
    14 years 9 months ago
    Conditional random fields for multi-agent reinforcement learning
    Download  www.machinelearning.org
    Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
    Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
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