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CaseBased Learning of Applicability Conditions for Stochastic Explanations
"... Abstract. This paper studies the problem of explaining events in stochastic environments. We explore three ideas to address this problem: (1) Using the notion of Stochastic Explanation, which associates with any event a probability distribution over possible plausible explanations for the event. (2) ..."
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Cited by 1 (0 self)
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) Retaining as cases (event, stochastic explanation) pairs when unprecedented events occur. (3) Learning the probability distribution in the stochastic explanation as cases are reused. We claim that a system using stochastic explanations reacts faster to abrupt changes in the environment than a system using
Contour Tracking By Stochastic Propagation of Conditional Density
, 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
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Cited by 658 (24 self)
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simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm  Conditional
Eliciting selfexplanations improves understanding
 Cognitive Science
, 1994
"... Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (selfexplaining) facilitates that integration process. Previously, selfexplanation has been shown to improve the acquisition of problemsolving skills when studying workedout examples. ..."
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Cited by 556 (22 self)
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Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (selfexplaining) facilitates that integration process. Previously, selfexplanation has been shown to improve the acquisition of problemsolving skills when studying workedout examples
Stochastic Perturbation Theory
, 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
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Cited by 886 (35 self)
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. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a firstorder perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variation in the perturbed quantity. Up to the higherorder terms that are ignored in the expansion, these statistics tend to be more realistic than perturbation bounds obtained in terms of norms. The technique is applied to a number of problems in matrix perturbation theory, including least squares and the eigenvalue problem. Key words. perturbation theory, random matrix, linear system, least squares, eigenvalue, eigenvector, invariant subspace, singular value AMS(MOS) subject classifications. 15A06, 15A12, 15A18, 15A52, 15A60 1. Introduction. Let A be a matrix and let F be a matrix valued function of A. Two principal problems of matrix perturbation theory are the following. Given a matrix E, pr...
TransformationBased ErrorDriven Learning and Natural Language Processing: A Case Study in PartofSpeech Tagging
 Computational Linguistics
, 1995
"... this paper, we will describe a simple rulebased approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learni ..."
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Cited by 916 (7 self)
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this paper, we will describe a simple rulebased approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study
CONDENSATION  conditional density propagation for visual tracking
 International Journal of Computer Vision
, 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously appli ..."
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Cited by 1499 (12 self)
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The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously
ExplanationBased Learning: An Alternative View
 Machine Learning
, 1986
"... Key words: machine learning, concept acquisition, explanationbased learning Abstract. In the last issue of this journal Mitchell, Keller, and KedarCabelli presented a unifying framework for the explanationbased approach to machine learning. While it works well for a number of systems, the framewo ..."
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Cited by 406 (20 self)
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Key words: machine learning, concept acquisition, explanationbased learning Abstract. In the last issue of this journal Mitchell, Keller, and KedarCabelli presented a unifying framework for the explanationbased approach to machine learning. While it works well for a number of systems
Gaussian processes for machine learning
 in: Adaptive Computation and Machine Learning
, 2006
"... Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperpar ..."
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Cited by 631 (2 self)
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Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn
The Valuation of Options for Alternative Stochastic Processes
 Journal of Financial Economics
, 1976
"... This paper examines the structure of option valuation problems and develops a new technique for their solution. It also introduces several jump and diffusion processes which have nol been used in previous models. The technique is applied lo these processes to find explicit option valuation formulas, ..."
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Cited by 661 (4 self)
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This paper examines the structure of option valuation problems and develops a new technique for their solution. It also introduces several jump and diffusion processes which have nol been used in previous models. The technique is applied lo these processes to find explicit option valuation formulas, and solutions to some previously unsolved problems involving the pricing ofsecurities with payouts and potential bankruptcy. 1.
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