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Bounded Explanation and Inductive Refinement For Acquiring Control Knowledge
 In Proceedings of the Third International Workshop on Knowledge Compilation and Speedup Learning
, 1993
"... One approach to learning control knowledge from a problem solving trace consists of generating explanations for the local decisions made during the search process. ..."
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Cited by 5 (4 self)
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One approach to learning control knowledge from a problem solving trace consists of generating explanations for the local decisions made during the search process.
Bucket Elimination: A Unifying Framework for Probabilistic Inference
, 1996
"... Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing in ..."
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Cited by 294 (27 self)
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Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing
Bucket Elimination: A Unifying Framework for Reasoning
"... Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many problemsolving and reasoning tasks. Algorithms such as directionalresolution for propositional satisfiability, adaptiveconsistency for constraint satisfaction, Fourier and Gaussian elimination ..."
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Cited by 298 (58 self)
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, finding the most probable explanation, and expected utility maximization. These algorithms share the same performance guarantees; all are time and space exponential in the inducedwidth of the problem's interaction graph. While elimination strategies have extensive demands on memory, a contrasting
Save More Tomorrow: Using Behavioral Economics to Increase Employee Saving.” http://faculty.chicagogsb.edu/richard.thaler/research/SMarT14.pdf
, 2001
"... As firms switch from definedbenefit plans to definedcontribution plans, employees bear more responsibility for making decisions about how much to save. The employees who fail to join the plan or who participate at a very low level appear to be saving at less than the predicted life cycle savings r ..."
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Cited by 296 (4 self)
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rates. Behavioral explanations for this behavior stress bounded rationality and selfcontrol and suggest that at least some of the lowsaving households are making a mistake and would welcome aid in making decisions about their saving. In this paper, we propose such a prescriptive savings program
SIGNIFICANCE AND EXPLANATION
, 1983
"... ABSTRACT.3 For a system of differentiable convex inequalities, a new bound is given for the absolute error in an infeasible point in terms of the absolute residual. By using this bound a condition number is defined for the system of inequalities which gives a bound for the relative error in an infea ..."
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ABSTRACT.3 For a system of differentiable convex inequalities, a new bound is given for the absolute error in an infeasible point in terms of the absolute residual. By using this bound a condition number is defined for the system of inequalities which gives a bound for the relative error
Prediction Games and Arcing Algorithms
, 1997
"... The theory behind the success of adaptive reweighting and combining algorithms (arcing) such as Adaboost (Freund and Schapire [1995].[1996]) and others in reducing generalization error has not been well understood. By formulating prediction, both classification and regression, as a game where one pl ..."
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Cited by 169 (0 self)
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of the error over the training set. A bound on the generalization error for the combined predictors in terms of their maximum error is proven that is sharper than bounds to date. Arcing algorithms are described that converge to the optimal strategy. Schapire et.al. [1997] offered an explanation of why Adaboost
On the Margin Explanation of Boosting Algorithms
"... Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bound for the generalization error of any voting classifier in terms of the margin distribution over the training data. Howe ..."
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Cited by 12 (9 self)
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Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bound for the generalization error of any voting classifier in terms of the margin distribution over the training data
Constraint Satisfaction, Bounded Treewidth, and FiniteVariable Logics
, 2002
"... We systematically investigate the connections between constraint satisfaction problems, structures of bounded treewidth, and definability in logics with a finite number of variables. We first show that constraint satisfaction problems on inputs of treewidth less than k are definable using Datalog ..."
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Cited by 71 (12 self)
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Datalog programs with at most k variables; this provides a new explanation for the tractability of these classes of problems. After this, we investigate constraint satisfaction on inputs that are homomorphically equivalent to structures of bounded treewidth.
Rhythmic Stability As Explanation Of Category Size
"... A measure is proposed for stability and the structural integration of a percept for a perceived temporal sequence. This measure is an elaboration of a theory of rhythm perception, named DECO, based on time intervals between any pair of onsets in a temporal pattern. More specifically, i t specifies b ..."
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Cited by 4 (0 self)
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bounds between successive intervals whose durations relate as simple integer ratios. The definition of the internal stability of a mental representation of rhythm depends on the contributions of these bounds. This theory i s in accordance with the responses when subjects were asked t o identify rhythmic
On the Doubt about Margin Explanation of Boosting
"... Margin theory provides one of the most popular explanations to the success of AdaBoost, where the central point lies in the recognition that margin is the key for characterizing the performance of AdaBoost. This theory has been very influential, e.g., it has been used to argue that AdaBoost usually ..."
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Cited by 3 (2 self)
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Margin theory provides one of the most popular explanations to the success of AdaBoost, where the central point lies in the recognition that margin is the key for characterizing the performance of AdaBoost. This theory has been very influential, e.g., it has been used to argue that AdaBoost usually
Results 1  10
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1,086