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Browsing Technical Reports (Electrical Engineering and Computer Science) by Author "Dietterich, Thomas Glen"

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Browsing Technical Reports (Electrical Engineering and Computer Science) by Author "Dietterich, Thomas Glen"

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  • Oregon State University. Dept. of Computer Science; Margineantu, Dragos D. (Dragos Dorin); Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2000)
    Many machine learning applications require classifiers that minimize an asymmetric cost function rather than the misclassification rate, and several recent papers have addressed this problem. However, these papers h ...
  • Oregon State University. Dept. of Computer Science; Restificar, Angelo C.; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2013-01-29)
    An important challenge in machine learning is to find ways of learning quickly from very small amounts of training data. The only way to learn from small data samples is to constrain the learning process by exploiting ba ...
  • Oregon State University. Dept. of Computer Science; Wu, Pengcheng; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2004)
    The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second, auxiliary, source of data is available ...
  • Oregon State University. Dept. of Computer Science; Bayer-Zubek, Valentina; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2004)
    This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic ...
  • Oregon State University. Dept. of Computer Science; Stumpf, Simone; Rajaram, Vidya; Li, Lida; Wong, Weng-Keen; Burnett, Margaret, 1949-; Dietterich, Thomas Glen; Sullivan, Erin; Herlocker, Jonathan Lee (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2007)
    Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple "right/wrong" judgments ...
  • Oregon State University. Dept. of Computer Science; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2003-05-26)
    What is the relationship between learning and reasoning? Much recent work in machine learning has been criticized for focusing on learning and ignoring reasoning. This paper attempts to describe the various ways in which ...
  • Oregon State University. Dept. of Computer Science; Dietterich, Thomas Glen; Langley, Pat (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2003-05-11)
    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 b ...
  • Oregon State University. Dept. of Computer Science; Bayer, Valentina; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2000)
    This paper introduces the even-odd POMDP, an approximation to POMDPs in which the world is assumed to be fully observable every other time step. The even-odd POMDP can be converted into an equivalent MDP, the 2MDP, whos ...
  • Oregon State University. Dept. of Computer Science; Zubek, Valentina Bayer; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2004-07-05)
    This paper introduces the even-odd POMDP an approximation to POMDPs Partially Observable Markov Decision Problems in which the world is assumed to be fully observable every other time step. This approximation works wel ...
  • Oregon State University. Dept. of Computer Science; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2006-04-30)
    Summary: Four proposed metrics: [1] average relative reduction in training time (sample size, number of training experiences) [2] jumpstart (initial advantage of transfer algorithm) [3] handicap (how long it takes the ...
  • Oregon State University. Dept. of Computer Science; Zubek, Valentina Bayer; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2004)
    This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to formulate this as a Markov Decision Proc ...
  • Oregon State University. Dept. of Computer Science; Stumpf, Simone; Burnett, Margaret, 1949-; Dietterich, Thomas Glen; Johnsrude, Kevin; Herlocker, Jonathan Lee (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2005)
    This paper presents qualitative results from interviews with knowledge workers about their recovery strategies after interruptions. Special focus is given to when these strategies fail due to the nature of the interrupti ...
  • Oregon State University. Dept. of Computer Science; Stumpf, Simone; Rajaram, Vidya; Li, Lida; Burnett, Margaret, 1949-; Dietterich, Thomas Glen; Sullivan, Erin; Drummond, Russell; Herlocker, Jonathan Lee (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2006-10-02)
    There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource--the users themselves--could somehow work hand-in-hand with machine learning systems, th ...
  • Oregon State University. Dept. of Computer Science; Zubek, Valentina Bayer; Dietterich, Thomas Glen (Corvallis, OR : Oregon State University, Dept. of Computer Science, 2004)
    A common heuristic for solving Partially Observable Markov Decision Problems POMDPs is to first solve the underlying Markov Decision Process MDP and then construct a POMDP policy by performing a fixed depth lookahead sea ...

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