Index Catalog // ScholarsArchive@OSU

Creator:: Oregon State University. Dept. of Computer Science and Dietterich, Thomas Glen
Abstract:: 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 no-transfer algorithm to overcome the jumpstart) [4] asymptotic advantage (how much better the transfer learning algorithm does in the...
Resource Type:: Research Paper
Full Text:: e r f o r m a n c e learning curve for algorithm B learning curve for algorithm AB p*AB

Creator:: Oregon State University. Dept. of Computer Science, Zubek, Valentina Bayer, and Dietterich, Thomas Glen
Abstract:: 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 well for problems with a delayed need to observe. The even-odd POMDP can be converted into an equivalent...
Resource Type:: Research Paper
Full Text:: �� POMDP Notations A POMDP is a tuple hS� A� O� Ptr�SjS�A�� Pobs�OjS�A�� R�SjS�A�� i where S is the set

Creator:: Oregon State University. Dept. of Computer Science, Zubek, Valentina Bayer, and Dietterich, Thomas Glen
Abstract:: 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 search in the POMDP and evaluating the leaf nodes using the MDP value function. A problem with...
Resource Type:: Research Paper
Full Text:: Notations A POMDP is a tuple hS� A� O� Ptr�SjS�A�� Pobs�OjS�A�� R�SjS�A�� i where S is the set of states

Creator:: Oregon State University. Dept. of Computer Science, Bayer, Valentina, and Dietterich, Thomas Glen
Abstract:: 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, whose value function, V*[subscript 2MDP], can be combined online with a 2-step lookahead search...
Resource Type:: Research Paper
Full Text:: tuple hS; A; O; Ptr(SjS;A); Pobs(OjS;A); R(SjS;A); i where S is the set of states in the world, A is

Creator:: Oregon State University. Dept. of Computer Science and Dietterich, Thomas Glen
Abstract:: 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 machine learning research has (and has not) incorporated reasoning. The paper argues that there are important...
Resource Type:: Research Paper
Full Text:: : Qπ ∗ (x, a) = ∑ x′ P (x′|x, a) [ R(x, a,x′) + max a′ Qπ ∗ (x′, a′) ] , (2) where R(x, a,x′) is

Creator:: Oregon State University. Dept. of Computer Science, Bayer-Zubek, Valentina, and Dietterich, Thomas Glen
Abstract:: 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 decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected...
Resource Type:: Research Paper

Creator:: Oregon State University. Dept. of Computer Science, Wu, Pengcheng, and Dietterich, Thomas Glen
Abstract:: 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 drawn from a different distribution. This auxiliary data is more plentiful, but of significantly lower quality,...
Resource Type:: Research Paper
Full Text:: 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 1 2 3 4 5 6 E rr or r at e Number of

Creator:: Dietterich, Thomas Glen, Ashenfelter, Adam J., and Bulatov, Yaroslav
Abstract:: Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such applications as part-of-speech tagging, text-to-speech mapping, protein and DNA sequence analysis, and information extraction from web pages. However, existing learning algorithms are slow, particularly...
Resource Type:: Conference Proceeding
Full Text:: R es id ue s C or re ct Iterations TreeCRF-V Mallet-V Qian-Sejnowski Figure 1: Protein

Creator:: Oregon State University. Dept. of Computer Science, Zubek, Valentina Bayer, and Dietterich, Thomas Glen
Abstract:: 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 Process in which the transition model is learned from the training data. Specifically we assume a set...
Resource Type:: Research Paper
Full Text:: 15000 20000 25000 0 2 4 6 8 10 m em or y at c on ve rg en ce ( O R + A N D n od es

ScholarsArchive@OSU