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task_solution_corpus
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    <definition>This is a frame for representing relations between ML Task and method that solves it. The solution method could be wider described. The method or collateral problems are probably described in reference article.</definition>
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            <text>Indeed, MCTS has been recently used by Gaudel and Sebag (2010) in their FUSE (Feature Uct SElection) system to perform feature selection.</text>
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            <text>In LP we used the 1-vs-1 method to solve the multi-class problem.</text>
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            <text>In order to solve the combinatorial feature selection problem, we propose to model feature selection and classification as a single sequential Markov Decision Process (MDP).</text>
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            <text>In this paper, we solve the call admission control and routing problem in multimedia networks via reinforcement learning (RL).</text>
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            <text>Furthermore, we show that if a symbolic machine learning method is used to solve the individual learning problems, the approach is also capable of generating concise explanations for the detected outliers.</text>
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            <text>We describe a convex duality for this class of methods and propose numerical algorithms to solve the derived dual learning problem.</text>
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            <text>We show that mass estimation solves problems effectively in tasks such as information retrieval, regression and anomaly detection.</text>
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            <text>It employs the Error-Correcting Output Code (ECOC) method to convert a multi-class learning problem into a set of binary classification problems, and applies the AdaBoost algorithm to solve them efficiently.</text>
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            <text>In this paper, we apply coordinate descent methods to solve the dual form of logistic regression and maximum entropy.</text>
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            <text>The authors develop an Expectation Maximization (EM) algorithm to solve the optimization problem, which allows finding the clustering solutions in sequence.</text>
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task_solution_corpus.txt · Last modified: 2016/03/16 11:38 by pj