Search Constraints

Start Over
Filtering by: Creator Congalton, Russell G. Remove constraint Creator: Congalton, Russell G. Subject Machine learning Remove constraint Subject: Machine learning

Search Results

  • Structured gradient boosting

    Creator:
    Parker, Charles (Charles Lincoln)
    Abstract:
    The goal of many machine learning problems can be formalized as the creation of a function that can properly classify an input vector, given a set of examples of that function. While this formalism has produced a number of success stories, there are notable situations in which it fails. One...
    Resource Type:
    Dissertation
    Full Text:
    random fields for the same task. above graph and the function g gives feature values for all cliques of

  • Rare category detection using hierarchical mean shift

    Creator:
    Vatturi, Pavan Kumar
    Abstract:
    Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
    Resource Type:
    Masters Thesis
    Full Text:
    =1 (x− xi)k′(‖x− xi h ‖2) = ck,gf̂h,G(x) ⎡ ⎢⎢⎢⎢⎣ n∑ i=1 xig(‖x− xi h ‖2) n∑ i=1 g(‖x− xi h

  • Methods for cost-sensitive learning

    Creator:
    Margineantu, Dragos D.
    Abstract:
    Many approaches for achieving intelligent behavior of automated (computer) systems involve components that learn from past experience. This dissertation studies computational methods for learning from examples, for classification and for decision making, when the decisions have different non-zero costs associated with them. Many practical applications of learning algorithms, including transaction...
    Resource Type:
    Dissertation
    Full Text:
    : -'<"-----~~-_.,,__, ____,,._...,...---:::....::....-~-vo""'1,...L...L."":>.....>,E¥"W":~""".,,.=----------- Thomas G

  • 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

  • Toward end-user debugging of machine-learned programs

    Creator:
    Kulesza, Todd
    Abstract:
    The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters because without the ability to fix errors, users may find that the learned program’s errors...
    Resource Type:
    Masters Thesis

  • Predictive mapping of landtype association maps in three Oregon national forests

    Creator:
    Peterman, Wendy L.
    Abstract:
    This study explores the use of predictive mapping techniques in developing Landtype Association (LTA) maps for use in natural resource management. These maps are produced for the USDA Forest Service on a regional basis at a 1:100,000 scale. The goal of this study is to develop and test a method...
    Resource Type:
    Masters Thesis
    Full Text:
    predicted data (Congalton, 1991). It is derived from the formula (Cohen, 1960

  • 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:
    Network Learning with Qualitative Models. Abstract approved: Thomas G. Dietterich Machine learning

  • 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:
    Page 5.5 Investigating the scalability of evaluation functions (G and D), com- bination of evaluation

  • Toward harnessing user feedback for machine learning

    Creator:
    Stumpf, Simone, Rajaram, Vidya, Li, Lida, Burnett, Margaret, 1949-, Dietterich, Thomas Glen, Sullivan, Erin, Drummond, Russell, and Herlocker, Jonathan Lee
    Abstract:
    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, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve...
    Resource Type:
    Technical Report
    Full Text:
    Simone Stumpf, Vidya Rajaram, Lida Li, Margaret Burnett, Thomas Dietterich, Erin Sullivan, Russell

  • 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:
    NP-hard [Russell and Norvig, 1995]. For example, none of the existing exact inference systems are