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680,750
Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment
 Psychological Review
, 1983
"... Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents, P(A) and.P(B), because the extension (or the possibility set) of the conjunction is included in the exten ..."
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Cited by 427 (4 self)
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Perhaps the simplest and the most basic qualitative law of probability is the conjunction rule: The probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents, P(A) and.P(B), because the extension (or the possibility set) of the conjunction is included
Efficient semantic matching
, 2004
"... We think of Match as an operator which takes two graphlike structures and produces a mapping between semantically related nodes. We concentrate on classifications with tree structures. In semantic matching, correspondences are discovered by translating the natural language labels of nodes into prop ..."
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Cited by 817 (67 self)
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into propositional formulas, and by codifying matching into a propositional unsatisfiability problem. We distinguish between problems with conjunctive formulas and problems with disjunctive formulas, and present various optimizations. For instance, we propose a linear time algorithm which solves the first class
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
 MACHINE LEARNING
, 1999
"... Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and realworld datasets. We review these algorithms and describe a large empirical study comparing several variants in co ..."
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Cited by 695 (2 self)
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in conjunction with a decision tree inducer (three variants) and a NaiveBayes inducer.
The purpose of the study is to improve our understanding of why and
when these algorithms, which use perturbation, reweighting, and
combination techniques, affect classification error. We provide a
bias and variance
Ontology Development 101: A Guide to Creating Your First Ontology
, 2001
"... In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the WorldWide Web ..."
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Cited by 806 (5 self)
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Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language
Concurrent Constraint Programming
, 1993
"... This paper presents a new and very rich class of (concurrent) programming languages, based on the notion of comput.ing with parhal information, and the concommitant notions of consistency and entailment. ’ In this framework, computation emerges from the interaction of concurrently executing agent ..."
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Cited by 502 (16 self)
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This paper presents a new and very rich class of (concurrent) programming languages, based on the notion of comput.ing with parhal information, and the concommitant notions of consistency and entailment. ’ In this framework, computation emerges from the interaction of concurrently executing
Symbolic Model Checking for Realtime Systems
 INFORMATION AND COMPUTATION
, 1992
"... We describe finitestate programs over realnumbered time in a guardedcommand language with realvalued clocks or, equivalently, as finite automata with realvalued clocks. Model checking answers the question which states of a realtime program satisfy a branchingtime specification (given in an ..."
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Cited by 574 (50 self)
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We describe finitestate programs over realnumbered time in a guardedcommand language with realvalued clocks or, equivalently, as finite automata with realvalued clocks. Model checking answers the question which states of a realtime program satisfy a branchingtime specification (given
Computational LambdaCalculus and Monads
, 1988
"... The calculus is considered an useful mathematical tool in the study of programming languages, since programs can be identified with terms. However, if one goes further and uses fijconversion to prove equivalence of programs, then a gross simplification 1 is introduced, that may jeopardise the ..."
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Cited by 505 (7 self)
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The calculus is considered an useful mathematical tool in the study of programming languages, since programs can be identified with terms. However, if one goes further and uses fijconversion to prove equivalence of programs, then a gross simplification 1 is introduced, that may jeopardise
A Theory of Diagnosis from First Principles
 ARTIFICIAL INTELLIGENCE
, 1987
"... Suppose one is given a description of a system, together with an observation of the system's behaviour which conflicts with the way the system is meant to behave. The diagnostic problem is to determine those components of the system which, when assumed to be functioning abnormally, will explain ..."
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Cited by 1117 (5 self)
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Suppose one is given a description of a system, together with an observation of the system's behaviour which conflicts with the way the system is meant to behave. The diagnostic problem is to determine those components of the system which, when assumed to be functioning abnormally
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
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Cited by 1231 (13 self)
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We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
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