Search Constraints

Start Over
Filtering by: Creator Sutton, Shan C. Remove constraint Creator: Sutton, Shan C. Subject Machine learning Remove constraint Subject: Machine learning

Search Results

  • Learning cost-sensitive diagnostic policies from data

    Creator:
    Zubek, Valentina Bayer
    Abstract:
    In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body tem- perature, weight, etc.) and laboratory measurements (white blood count, CT scan,...
    Resource Type:
    Dissertation
    Full Text:
    diagnostic policies that seek to minimize the expected total cost of diagnosis. c Copyright by Valentina

  • Monte-Carlo planning for probabilistic domains

    Creator:
    Bjarnason, Ronald V.
    Abstract:
    This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyze how these algorithms can resolve some of the shortcomings of traditional probabilistic planning methods. The direct policy search...
    Resource Type:
    Dissertation
    Full Text:
    cards is known, solving more than 82% of games. c©Copyright by Ronald Vance Bjarnason, Jr. December

  • Constraining Bayesian network learning with qualitative models

    Creator:
    Altendorf, Eric E.
    Abstract:
    Machine learning encompasses probabilistic and statistical techniques that can build models from large quantities of extensional information (examples) with minimal dependence on intensional information (domain knowledge). This focus of machine learning is reflected in the never-ending quest for "off-the-shelf" classifiers. To generalize to unseen data, however, we must make use...
    Resource Type:
    Masters Thesis
    Full Text:
    Gnuplot (Wade Holmes). I also thank Aron W. Culotta, Charles Sutton, and Jerod Weinman from the Mallet

  • Combining coordination mechanisms to improve performance in multi-robot teams

    Creator:
    Nasroullahi, Ehsan
    Abstract:
    Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can...
    Resource Type:
    Masters Thesis
    Full Text:
    %. c©Copyright by Ehsan Nasroullahi March 9, 2012 All Rights Reserved Combining Coordination

  • Learning and reasoning

    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:
    at run time, the patient (or doctor) can specify a cost matrix C(ŷ, y) which specifies the cost of

  • Learning diagnostic policies from examples by systematic search

    Creator:
    Oregon State University. Dept. of Computer Science and Zubek, Valentina Bayer
    Abstract:
    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....
    Resource Type:
    Research Paper
    Full Text:
    transition is Ptr�s �js� xn� � P �xn � vnjs�� and the expected cost is C�xn�� which is the cost of test xn

  • Representations and algorithms for efficient inference in Bayesian networks

    Creator:
    Takikawa, Masami
    Abstract:
    Bayesian networks are used for building intelligent agents that act under uncertainty. They are a compact representation of agents' probabilistic knowledge. A Bayesian network can be viewed as representing a factorization of a full joint probability distribution into the multiplication of a set of conditional probability distributions. Independence of causal...
    Resource Type:
    Dissertation
    Full Text:
    . One factoring is given in the formula: P(D, E) = [E[E[E[P(EIC) x P (DP, C)] x P (CIA)] x P(BIA)1

  • Machine learning for cognitive networks : technology assessment and research challenges

    Creator:
    Oregon State University. Dept. of Computer Science, Dietterich, Thomas Glen, and Langley, Pat
    Abstract:
    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...
    Resource Type:
    Research Paper
    Full Text:
    (Kaelbling et al., 1996; 5 Sutton & Barto, 1998). This approach involves propagating rewards backward

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
    Resource Type:
    Dissertation
    Full Text:
    install a predictor for the requested prediction problem. c©Copyright by Kshitij Judah March 21, 2014

  • Integrating learning and search for structured prediction

    Creator:
    Doppa, Janardhan Rao
    Abstract:
    We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
    Resource Type:
    Dissertation
    Full Text:
    cost function C and returns the minimum-cost solution. There are three key learning challenges in this