Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference, San Mateo, CA: Morgan Kaufmann. Neapolitan, R.E. and J.R. Kenenvan, (1991) Investigation of Variances in Belief Networks, Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence, San Mateo, CA: Morgan Kaufmann.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....(Vapnik 1982; Vapnik 1998) In the past, machine learning mainly focused on the problems of classification and regression estimation. As will be seen later, the problem of ordinal regression shares characteristics of both these tasks. Let us consider the basic assumptions made in (supervised) machine learning (Vapnik 1998): Given an i.i.d. sample S = f(x i ; y i )g i=1 P XY where P XY = Q i=1 PXY , and a set H of mappings h from X to Y , a learning procedure selects one mapping h such that using a predefined loss l : Y Theta Y 7 R the risk functional R(h ) is minimized. In ....

....reduces to finding a utility function that best reflects the preferences induced by the unknown distribution PXY . Our learning procedure on pairs of objects is an application of the large margin idea known from data dependent Structural Risk Minimization (Shawe Taylor et al. 1996; Cristianini et al. 1998). The resulting algorithm is similar to Support Vector Machines (SVM) Cortes and Vapnik 1995; Vapnik 1995) which became popular in recent years due to their good generalization properties (Cortes and Vapnik 1995; Smola 1996; Scholkopf 1997; Girosi 1997; Wahba 1997; Weston et al. 1997; Pontil and ....

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In Advances in Neural Information Processing Systems, San Mateo, CA. Morgan Kaufmann. Herbrich, R., T. Graepel, P. Bollmann-Sdorra, and K. Obermayer (1998).

.... example, acquire knowledge by analyzing a user s normal interaction with a system [Dent et al. 1992; Mitchell et al. 1990; Wilkins, 1990] Some systems learn from solutions given by the user, while others display a proposed solution that the user can accept, correct, or rate [Tecuci, 1992; Baudin et al. 1993; Dent et al. 1992; Maes and Kozierok, 1993] An advantage of these approaches is that they are nonintrusive, and users do not need to understand the underlying learning process beyond sometimes the fact that it is analyzing their examples. To learn under this limited style of interaction, the ....

In Principles of Semantic Networks: Explorations in the Representation of Knowledge, ed. J. Sowa. San Mateo, CA: Morgan Kaufmann. Maes, P. and Kozierok, R. 1993. Learning interface agents.

....grammars. Assuming that a satisfactory solution for the selection of pattern primitives and the task of recognizing grammatical sentences can be found for a given application, learning to recognize classes of patterns reduces to learning the corresponding grammars (or equivalently the corresponding automata) the grammar inference problem (see Fu, 1982; Miclet, 1986; Parekh Honavar, 1993 for details) In many practical problems, it becomes necessary to supplement classical grammars which are purely syntactic in nature with semantic information. Attributed grammars offer such a model in which semantics of symbols can be learned in terms of functions that ....

....the set of primitives used by adding potentially useful or meaningful compositions of existing primitive patterns. The primitives so added have an effect analogous to extending the vocabulary of natural language by defining new terms or concepts. Similar chunking mechanisms have been explored in symbol processing systems (Laird, Rosenbloom, Newell, 1986; Uhr Vossler, 1963) generative learning algorithms for connectionist systems (Honavar Uhr, 1993a) and in some evolutionary learning systems (Koza, 1992) It is possible to combine the template based and grammar based approaches to syntactic pattern recognition. For example, a pre specified ....

In: Readings in Knowledge Acquisition and Learning. Buchanan, B. G., & Wilkins, D. C. San Mateo, California: Morgan Kaufmann. Miclet, L. (1986). Structural Methods in Pattern Recognition. New York: Springer-Verlag.

....traditional GAs, or any other similarity based search technique . The literature on Deception in GAs is growing rapidly (Battle Vose, 1991; Davidor, 1990; Deb and Goldberg, 1992; Goldberg 1989a, 1989b, 1989c, 1991, 1992; Goldberg, Deb and Korb, 1990, 1991; Goldberg, Deb and Clark, 1992; Liepins Vose, 1990, 1991; Mason, 1991; Whitley, 1991, 1992) so this is clearly a topic that deserves careful scrutiny. In previous papers (Grefenstette and Baker, 1989; Grefenstette, 1991) we have raised some questions about this approach to the analysis of GAs, and others have begun to express similar concerns (Forrest ....

....average utility and is not disrupted by crossover. Therefore, such schemata indicate the area within the search space that the GA explores, and hence it is important that, at some stage, these schema contain the object of search. Problems for which this is not true are called deceptive. Liepins and Vose, 1991) or It has been shown that genetic algorithms work well when building blocks, short, low order . schemata with above average fitness values, combine to form optimal or near optimal solutions. Homaifar et. al, 1991) When such statements are followed by the analysis of static hyperplane ....

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In Foundations of Genetic Algorithms, G. J. E. Rawlins (Ed.), San Mateo, CA: Morgan Kaufmann. Mason, A. J. (1991). Partition coefficients, static deception and deceptive problems for non-binary alphabets. Proceedings of the Fourth International Conference of Genetic Algorithms (pp. 210-214). San Mateo, CA: Morgan Kaufmann.

....of Markov Modeling of GAs The primary advantage of Markov modeling over other mother modeling methods is its completeness; the Markov models we have discussed account for all stochastic effects of all GA operators and track all GA dynamics. They are therefore exact models. As Vose has stated (Vose, 1995), there is really no distinction between the Markov chain and the GA it models. Yet Markov analysis suffers from two major drawbacks: 1) intractability (regarding the cost of modeling) and (2) focus on asymptotic behavior. The first limitation we have mentioned above: the number of states in ....

, Foundations of Genetic Algorithms 3

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Readings in Model-Based Diagnosis. San Mateo, CA: Morgan Kaufmann. Heckerman, D. 1995. A tutorial on learning bayesian networks. Technical Report MSR--TR--95--06, Microsoft Research.

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In Reasoning about Plans. San Mateo, CA: Morgan Kaufmann. 1--68. Chapman, D. 1987. Planning for conjunctive goals.

....Morgan Kaufmann, San Francisco, CA. coarsely grained parallelism is an island model in which there are a number of centralized EAs running in parallel. The focus is on designing and implementing useful migration mechanisms which allow exchange of information between the independently evolving local populations (Tanese 1989; Cohoon, Martin, and Richards 1991; Whitley and Starkweather 1990) This paper is the result of our interest in adapting EAs to effectively exploit fine grained architectures. In this case the focus is on parallelizing the algorithm itself and many candidate methods for doing so have been ....

....from both a communication overhead point of view and its biological plausibility. This requires introducing some sort of distance metric and or topology on the population so that the concept of a neighborhood can be defined. Metrics involving distance in genotype or phenotype spaces (such as sharing functions (Deb and Goldberg 1989)) generally require global statistics and are not easy to implement efficiently in a decentralized form. For finely grained parallel architectures a more natural approach is to introduce a topology in which individuals live on grid points and neighborhoods defined in terms of nearby grid points. ....

In Foundations of Genetic Algorithms, San Mateo, CA, pp. 69--93. Morgan Kaufmann. Goldberg, D. E., B. Korb, and K. Deb (1989). Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3, 493--530.

....into an adaptive succession of phenotypic forms. But evolution s role is not limited to taking care of the genetic influences on development. The environmental influences, too, must lead to adaptive changes, and evolution must also guarantee that this is generally true. Phenotypic plasticity [Scheiner, 1993] is a notion sometimes used to describe the role of evolution in constraining and guiding the environmental influences on the individual. What an individual inherits from its parents is a genotype. The genotype is initially encoded in the DNA contained in the egg cell. Through cell division and ....

In: Proceedings Third International Conference on Genetic Algorithms. San Mateo, CA, Morgan Kaufmann. Mondada F., E. Franzi, and P. Ienne. 1993. Mobile Robot miniaturisation: A tool for investigation in control algorithms. In: Proceedings of the Third International Symposium on Experimental Robotics.

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Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference, San Mateo, CA: Morgan Kaufmann. Neapolitan, R.E. and J.R. Kenenvan, (1991) Investigation of Variances in Belief Networks, Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence, San Mateo, CA: Morgan Kaufmann.

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In Advances in Neural Information Processing Systems, volume 1, pages 305--313, San Mateo, CA. Morgan Kaufmann. Pratt, L. Y. and Mostow, J. (1991). Direct transfer of learned information among neural networks. In Proceedings of the Ninth National Conference on Artificial Intelligence, pages 584--589, Anaheim, CA.

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In Advances in Neural Information Processing Systems, volume 2, pages 396--404, San Mateo, CA. Morgan Kaufmann. Maclin, R. and Shavlik, J. W. (1991). Refining domain theories expressed as finite-state automata. In Machine Learning: Proceedings of the Seventh International Workshop, pages 524--528, Evanston, IL. Morgan Kaufmann.

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Readings in Model-Based Diagnosis. San Mateo, CA: Morgan Kaufmann. Kautz, H.; Selman, B.; Coen, M.; Ketchpel, S.; and Ramming, C. 1994. An experiment in the design of software agents. In Proceedings of AAAI-94.

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