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Filtering by: Creator Cerbone, G. (Giuseppe) Remove constraint Creator: Cerbone, G. (Giuseppe) Creator Dietterich, Thomas G. Remove constraint Creator: Dietterich, Thomas G.

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  • Learning optimal search control for engineering optimization

    Creator:
    Cerbone, Giuseppe and Dietterich, Thomas G.
    Abstract:
    This paper applies learning techniques to make engineering optimization more efficient and reliable. When the function to be optimized is highly non-linear, the search space generally forms several disjoint convex regions . Unless gradient-descent search is begun in the right region, the solution found will be suboptimal. This paper formalizes...
    Resource Type:
    Technical Report
    Full Text:
    Engineering Optimization Giuseppe Cerbone Thomas G. Dietterich Oregon State University Computer Science

  • Inductive and numerical methods in knowledge compilation

    Creator:
    Cerbone, Giuseppe and Dietterich, Thomas G.
    Abstract:
    Many important application problems can be formalized as constrained non-linear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. Furthermore, they do not provide much insight into the structure of the problem space. This paper describes a method for discovering efficient rules that...
    Resource Type:
    Technical Report
    Full Text:
    Methods in Knowledge Comp\lation Giuseppe Cerbone Thomas G. Dietterich Computer Science Department

  • Varieties of operationality

    Creator:
    Dietterich, Thomas G. and Bennett, James S.
    Abstract:
    In recent papers on machine learning, the term 'operationalization' has been used to describe the purpose of the learning process. In particular, explanation-based learning systems are said to 'operationalize' the given target concept. Unfortunately, the exact meaning of this term has varied from one paper to another, and frequently the...
    Resource Type:
    Technical Report
    Full Text:
    Operationality Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis

  • Induction over explanations : a method that exploits domain knowledge to learn from examples

    Creator:
    Flann, Nicholas S. and Dietterich, Thomas G.
    Abstract:
    We introduce five criteria by which to judge the suitability of a method for solving the problem of learning concepts from examples: correctness (the correct concept should be identified), performance efficiency (the learned definition should be efficient to apply to the performance task), flexibility (the method should be able to...
    Resource Type:
    Technical Report
    Full Text:
    Method that Exploits Do~ain Knowledge to Learn from Examples Nicholas S. Flann Thomas G. Dietterich

  • A study of explanation-based methods for inductive learning

    Creator:
    Flann, Nicholas S. and Dietterich, Thomas G.
    Abstract:
    This paper formalizes a new learning from examples problem: identifying a correct concept definition from positive examples such that the concept is some specialization of a target concept defined by a domain theory. This paper describes an empirical study that evaluates three methods for solving this problem: explanation based generalization...
    Resource Type:
    Technical Report
    Full Text:
    -Based Methods for I~ductive Learning Nicholas S. Flann Thomas G. Dietterich Department of Computer