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Disjunctive Normal Forms and Local Exceptions
, 2003
"... All classical λterms typable with disjunctive normal forms are shown to share a common computational behavior: they implement a local exception handling mechanism whose exact workings depend on the tautology. Equivalent and more efficient control combinators are described through a speci ..."
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Cited by 4 (2 self)
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All classical λterms typable with disjunctive normal forms are shown to share a common computational behavior: they implement a local exception handling mechanism whose exact workings depend on the tautology. Equivalent and more efficient control combinators are described through a
Disjunctive Normal Form for EventRecording Logic
"... Abstract. We consider the semantics equivalence between formulas and formulas in disjunctive normal form. In the settings of the standard µcalculus, formulas and disjunctive formulas are equivalent. This question is open for timed extensions of the µcalculus. Sorea has introduced a timed µcalculu ..."
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Abstract. We consider the semantics equivalence between formulas and formulas in disjunctive normal form. In the settings of the standard µcalculus, formulas and disjunctive formulas are equivalent. This question is open for timed extensions of the µcalculus. Sorea has introduced a timed µ
Disjunctive Normal Forms specify MultiException Handlers
"... Within a classical callbyname λcalculus, we prove, using Krivine’s realizability, that all terms typable with disjunctive normal forms (disjunctions of conjunctions of literals) share a common computational behaviour: they implement a multiexception handling mechanism whose exact geometry depend ..."
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Within a classical callbyname λcalculus, we prove, using Krivine’s realizability, that all terms typable with disjunctive normal forms (disjunctions of conjunctions of literals) share a common computational behaviour: they implement a multiexception handling mechanism whose exact geometry
Exponential blowup from conjunctive to disjunctive normal form
, 2002
"... Printable version of a sample proof that uses Lamport’s proof style [1], illustrating how structured proofs can be converted to HTML pages via LATEX2HTML enriched with extensions for Lamport’s proof style. Note that we try on purpose to carry out Lamport’s rule of thumb to “expand the proof until th ..."
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the lowest level statements are obvious, and then continue for one more level ” in order to illustrate the principles of structured proofs. Problem (cf. [2]): What is the disjunctive normal form of (x1∨y1)∧(x2 ∨ y2) ∧... ∧ (xn ∨ yn)? 1 Solution 1 (x1 ∨ y1) ∧ (x2 ∨ y2) ∧... ∧ (xn ∨ yn) ≡ (x1 ∧ x2
Solving QBF with Combined Conjunctive and Disjunctive Normal Form
 in Proc. of the 21st Nat. Conf. on Artificial Intelligence (AAAI
, 2006
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The Complexity of Minimizing Disjunctive Normal Form Formulas
, 1999
"... Contents 1 Introduction 3 2 Preliminaries 6 3 Computing a Minimum DNF 8 4 NP is Enough 11 5 Minimum Term DNF 13 5.2 The hA; Biversion . . . . . . . . . . . . . . . . . . . . . . . . 17 5.3 Masek's Result . . . . . . . . . . . . . . . . . . . . . . . . . . 18 6 Minimum Length DNF 32 6.1 Length ..."
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Contents 1 Introduction 3 2 Preliminaries 6 3 Computing a Minimum DNF 8 4 NP is Enough 11 5 Minimum Term DNF 13 5.2 The hA; Biversion . . . . . . . . . . . . . . . . . . . . . . . . 17 5.3 Masek's Result . . . . . . . . . . . . . . . . . . . . . . . . . . 18 6 Minimum Length DNF 32 6.1 Length vs. Terms . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.2 The Aversion . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 6.3 The hA; Biversion . . . . . . . . . . . . . . . . . . . . . . . . 35 6.4 The full truthtable version . . . . . . . . . . . . . . . . . . . 36 7 Minimum depth DNF 38 7.1 f is a Total Function . . . . . . . . . . . . . . . . . . . . . . . 40 7.2 f is a Partial Function . . . . . . . . . . . . . . . . . . . . . . 42 8 Approximation Hardness 42 8.1 Preserved Solution Values . . . . . . . . . . . . . . . . . . . . 43 8.2 Masek's Reduction . . . . . . . . . . . . . . . . . . . . . . . . 44 8.3 Reductions from X3C . . . . . . . . . . . . . . . . . . . . . . . 4
GENOTYPE PHENOTYPE MAPPING IN RNA VIRUSES DISJUNCTIVE NORMAL FORM LEARNING
"... RNA virus phenotypic changes often result from multiple alternative molecular mechanisms, where each mechanism involves changes to a small number of key residues. Accordingly, we propose to learn genotypephenotype functions, using Disjunctive Normal Form (DNF) as the assumed functional form. In this ..."
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RNA virus phenotypic changes often result from multiple alternative molecular mechanisms, where each mechanism involves changes to a small number of key residues. Accordingly, we propose to learn genotypephenotype functions, using Disjunctive Normal Form (DNF) as the assumed functional form
5 Learning Conjunctive Concepts 6 Learning Disjunctive Normal Forms
"... Machine Learning (ML) was defined in [3] as the discipline that aims to construct computer programs that learn from experience with respect to some class of tasks and performance measure, if its performance improves with experience. Prof. Dan A. Simovici (UMB) MACHINE LEARNING CS671 Part 1 3 / 44A ..."
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Machine Learning (ML) was defined in [3] as the discipline that aims to construct computer programs that learn from experience with respect to some class of tasks and performance measure, if its performance improves with experience. Prof. Dan A. Simovici (UMB) MACHINE LEARNING CS671 Part 1 3 / 44An essential feature of ML algorithms is their capability for generalization, defined as the ability of an algorithm to perform accurately on new, unseen examples after having trained on a learning data set. Typically, training examples come from some generally unknown probability distribution and the ML program has to extract from them something more general that allows it to produce useful predictions in new cases. Prof. Dan A. Simovici (UMB) MACHINE LEARNING CS671 Part 1 4 / 44ML and DM ML is a foundational discipline for Data Mining: the focus of ML is on prediction, based on knowledge learned from the training data; Data Mining focuses on efficient algorithms for discovery of unknown properties on the data. Prof. Dan A. Simovici (UMB) MACHINE LEARNING CS671 Part 1 5 / 44A sequence of length n on S is a function s: {0,...,n − 1} − → S. We denote such a sequence by (s(0),s(1),...,s(n − 1)). For n � 0, the set of sequences of length n of elements of S is denoted by Seqn(S). The set of all sequences of S is denoted by Seq(S) = ⋃ Seqn(S). n�0 Occasionally, when n � 1, we shall denote a sequence of length n as (s(1),...,s(n)).
AIisTRAcr AN EXTENDED DISJUNCTIVE NORMAL FORM APPROACH FOR OPTIMIZING RECURSIVE LOGIC QUERIES IN LOOSELY COUPLED ENVIRONMENTS
"... We present an approach to processing logic queries in loosely coupled environments. We emphasize the importance of the loose coupling technique as a practkal solution to provide deductive capabiities to existing DBMS+especially when an efficient access to a very large database is required in the. pr ..."
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. process of inferencing. We propose the Extended Disjunctive Normal Form (EDNF) as the basis of our approach. The EDNF is an extension of the disjunctive normal form of relational algebra expressions so as to include recursion. The EDNF is well suited for a loosely coupled environment, where an existing
An Incremental, Polynomialtime Algorithm to Induce Disjunctive Normal Form Representations for Propositional Concepts
, 1994
"... We review the notion of polynomial learnability (Valiant, 1984) for unrestricted DNF and present an alternative notion of the problemspecific incremental learnability (Oblow, 1992) of unrestricted DNF. We then present an incremental, polynomialtime algorithm for inducing disjunctive normal form ..."
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We review the notion of polynomial learnability (Valiant, 1984) for unrestricted DNF and present an alternative notion of the problemspecific incremental learnability (Oblow, 1992) of unrestricted DNF. We then present an incremental, polynomialtime algorithm for inducing disjunctive normal
Results 1  10
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