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• 1. [Article] Machine learning for cognitive networks : technology assessment and research challenges

  The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive ...

  Citation × Citation Citation Abstract Copy Title: Machine learning for cognitive networks : technology assessment and research challenges Author: Langley, Pat, Oregon State University. Dept. of Computer Science, Dietterich, Thomas Glen The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive networking and briefly discusses how aspects of those tasks fit these formulations. After this, it discusses challenges for machine learning research raised by Knowledge Plane applications and closes with proposals for the evaluation of learning systems developed for these problems. Title: Machine learning for cognitive networks : technology assessment and research challenges Author: Langley, Pat, Oregon State University. Dept. of Computer Science, Dietterich, Thomas Glen The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive networking and briefly discusses how aspects of those tasks fit these formulations. After this, it discusses challenges for machine learning research raised by Knowledge Plane applications and closes with proposals for the evaluation of learning systems developed for these problems. Close

• 2. [Article] Learning diagnostic policies from examples by systematic search

  A diagnostic policy species what test to perform next based on the results of previous tests and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the ...

  Citation × Citation Citation Abstract Copy Title: Learning diagnostic policies from examples by systematic search Author: Zubek, Valentina Bayer, Oregon State University. Dept. of Computer Science A diagnostic policy species what test to perform next based on the results of previous tests and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the costs of tests and (b) the costs of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process MDP. We investigate two types of algorithms for solving this MDP systematic search based on the AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed that control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions it discusses the factors that make systematic search feasible for diagnosis and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods. Title: Learning diagnostic policies from examples by systematic search Author: Zubek, Valentina Bayer, Oregon State University. Dept. of Computer Science A diagnostic policy species what test to perform next based on the results of previous tests and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the costs of tests and (b) the costs of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process MDP. We investigate two types of algorithms for solving this MDP systematic search based on the AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed that control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions it discusses the factors that make systematic search feasible for diagnosis and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods. Close