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156,925
Monotone Complexity
, 1990
"... We give a general complexity classification scheme for monotone computation, including monotone spacebounded and Turing machine models not previously considered. We propose monotone complexity classes including mAC i , mNC i , mLOGCFL, mBWBP , mL, mNL, mP , mBPP and mNP . We define a simple ..."
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Cited by 2837 (11 self)
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;enyi's nonmonotone result [Imm88, Sze87] that NL = coNL; this is a simple extension of the monotone circuit depth lower bound of Karchmer and Wigderson [KW90] for stconnectivity. We also consider mBWBP (monotone bounded width branching programs) and study the question of whether mBWBP is properly contained
Tabling for Nonmonotonic Programming
 Annals of Mathematics and Artificial Intelligence
, 1999
"... this paper we describe tabling as it is implemented in the XSB system ..."
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Cited by 73 (23 self)
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this paper we describe tabling as it is implemented in the XSB system
Monotone Simulations of Nonmonotone Proofs
, 2001
"... We show that an LK proof of size m of a monotone sequent (a sequent that contains only formulas in the basis ; ) can be turned into a proof containing only monotone formulas of size O(log m) and with the number of proof lines polynomial in m. Also we show that some interesting special cases, n ..."
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Cited by 21 (2 self)
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We show that an LK proof of size m of a monotone sequent (a sequent that contains only formulas in the basis ; ) can be turned into a proof containing only monotone formulas of size O(log m) and with the number of proof lines polynomial in m. Also we show that some interesting special cases
nonmonotone
, 2013
"... When more does not necessarily mean better: Healthrelated illfare comparisons with ..."
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When more does not necessarily mean better: Healthrelated illfare comparisons with
NonMonotonic . . . for Analyzing Semantics and Structural Properties of NonMonotonic Reasoning
, 1998
"... ..."
Linguistic Complexity: Locality of Syntactic Dependencies
 COGNITION
, 1998
"... This paper proposes a new theory of the relationship between the sentence processing mechanism and the available computational resources. This theory  the Syntactic Prediction Locality Theory (SPLT)  has two components: an integration cost component and a component for the memory cost associa ..."
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Cited by 486 (31 self)
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This paper proposes a new theory of the relationship between the sentence processing mechanism and the available computational resources. This theory  the Syntactic Prediction Locality Theory (SPLT)  has two components: an integration cost component and a component for the memory cost
Nonmonotonic Learning
 Inductive Logic Programming
, 1992
"... This paper addresses methods of specialising firstorder theories within the context of incremental learning systems. We demonstrate the shortcomings of existing firstorder incremental learning systems with regard to their specialisation mechanisms. We prove that these shortcomings are fundamental ..."
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Cited by 60 (12 self)
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to the use of classical logic. In particular, minimal "correcting " specialisations are not always obtainable within this framework. We propose instead the adoption of a specialisation scheme based on an existing nonmonotonic logic formalism. This approach overcomes the problems that arise
Monotone simulations of nonmonotone propositional proofs
 ELECTRONIC COLLOQUIUM ON COMPUTATIONAL COMPLEXITY, REPORT NO. 87
, 2000
"... We show that an LK proof of size m of a monotone sequent (a sequent that contains only formulas in the basis ∧, ∨) can be turned into a proof containing only monotone formulas of size m O(log m) and with the number of proof lines polynomial in m. Also we show that some interesting special cases, nam ..."
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We show that an LK proof of size m of a monotone sequent (a sequent that contains only formulas in the basis ∧, ∨) can be turned into a proof containing only monotone formulas of size m O(log m) and with the number of proof lines polynomial in m. Also we show that some interesting special cases
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (15 self)
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upper bounds known today. We show that the number of hypotheses that are combined by our algorithm is the smallest number possible. Other outcomes of our analysis are results regarding the representational power of threshold circuits, the relation between learnability and compression, and a method
Results 1  10
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156,925