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Learning Bayesian network structure from massive datasets: the “sparse candidate” algorithm
 In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI
, 1999
"... Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the sear ..."
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Cited by 247 (7 self)
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an algorithm that achieves faster learning by restricting the search space. This iterative algorithm restricts the parents of each variable to belong to a small subset of candidates. We then search for a network that satisfies these constraints. The learned network is then used for selecting better
Randomness Testing of the AES Candidate Algorithms
"... One of the criteria used to evaluate the AES candidate algorithms was their demonstrated suitability as random number generators. That is, the evaluation of their output utilizing statistical tests should not provide any means by which to computationally distinguish them from a truly random source. ..."
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Cited by 8 (0 self)
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One of the criteria used to evaluate the AES candidate algorithms was their demonstrated suitability as random number generators. That is, the evaluation of their output utilizing statistical tests should not provide any means by which to computationally distinguish them from a truly random source
Encryption Standard Candidate Algorithms Affiliation
, 1999
"... One of the criteria used to evaluate the Advanced Encryption Standard candidate algorithms was their demonstrated suitability as random number generators. That is, the evaluation of their output utilizing statistical tests should not provide any means by which to computationally distinguish them fro ..."
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One of the criteria used to evaluate the Advanced Encryption Standard candidate algorithms was their demonstrated suitability as random number generators. That is, the evaluation of their output utilizing statistical tests should not provide any means by which to computationally distinguish them
Performance of the AES Candidate Algorithms in Java
 In AES Candidate Conference
, 2000
"... We analyze the five remaining AES candidate algorithms MARS, RC6, Rijndael, Serpent, and Twofish as well as DES, Triple DES, and IDEA by examining independently developed Java implementations. We give performance measurement results on several platforms, list the memory requirements, and present a s ..."
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Cited by 4 (0 self)
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We analyze the five remaining AES candidate algorithms MARS, RC6, Rijndael, Serpent, and Twofish as well as DES, Triple DES, and IDEA by examining independently developed Java implementations. We give performance measurement results on several platforms, list the memory requirements, and present a
2. Requirements for Candidate Algorithm Submission Packages
"... ACTION: Notice and Request for nominations for candidate hash algorithms. SUMMARY: This notice solicits nominations from any interested party for candidate algorithms to be considered for SHA3, and specifies how to submit a nomination package. It presents the nomination requirements and the minimum ..."
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ACTION: Notice and Request for nominations for candidate hash algorithms. SUMMARY: This notice solicits nominations from any interested party for candidate algorithms to be considered for SHA3, and specifies how to submit a nomination package. It presents the nomination requirements
Mining Frequent Patterns without Candidate Generation: A FrequentPattern Tree Approach
 DATA MINING AND KNOWLEDGE DISCOVERY
, 2004
"... Mining frequent patterns in transaction databases, timeseries databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriorilike candidate set generationandtest approach. However, candidate set generation is still co ..."
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Cited by 1757 (64 self)
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Mining frequent patterns in transaction databases, timeseries databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriorilike candidate set generationandtest approach. However, candidate set generation is still
Finding Better Candidate Algorithms for PortfolioBased Planners
"... In order to construct a highperformance portfoliobased planner, a diverse set of candidate algorithms is needed. In our work, we are looking at the problem of constructing such candidate sets. Below we describe the ArvandHerd planner and use it to demonstrate the importance of selecting a candida ..."
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In order to construct a highperformance portfoliobased planner, a diverse set of candidate algorithms is needed. In our work, we are looking at the problem of constructing such candidate sets. Below we describe the ArvandHerd planner and use it to demonstrate the importance of selecting a candidate
Adaptive Constraint Satisfaction
 WORKSHOP OF THE UK PLANNING AND SCHEDULING
, 1996
"... Many different approaches have been applied to constraint satisfaction. These range from complete backtracking algorithms to sophisticated distributed configurations. However, most research effort in the field of constraint satisfaction algorithms has concentrated on the use of a single algorithm fo ..."
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Cited by 953 (43 self)
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Many different approaches have been applied to constraint satisfaction. These range from complete backtracking algorithms to sophisticated distributed configurations. However, most research effort in the field of constraint satisfaction algorithms has concentrated on the use of a single algorithm
gSpan: GraphBased Substructure Pattern Mining
, 2002
"... We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
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Cited by 649 (34 self)
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We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs
Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1055 (12 self)
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is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut
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