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835,718
Improving retrieval performance by relevance feedback
 Journal of the American Society for Information Science
, 1990
"... Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate ..."
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Cited by 749 (6 self)
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Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate
Improved Statistical Alignment Models
 In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics
, 2000
"... In this paper, we present and compare various singleword based alignment models for statistical machine translation. We discuss the five IBM alignment models, the HiddenMarkov alignment model, smoothing techniques and various modifications. ..."
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Cited by 593 (13 self)
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In this paper, we present and compare various singleword based alignment models for statistical machine translation. We discuss the five IBM alignment models, the HiddenMarkov alignment model, smoothing techniques and various modifications.
Eliciting selfexplanations improves understanding
 Cognitive Science
, 1994
"... Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (selfexplaining) facilitates that integration process. Previously, selfexplanation has been shown to improve the acquisition of problemsolving skills when studying workedout examples. ..."
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Cited by 556 (22 self)
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Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (selfexplaining) facilitates that integration process. Previously, selfexplanation has been shown to improve the acquisition of problemsolving skills when studying workedout examples
Comparison of Iterative Improvement Techniques for Schedule Optimization
 EUROPEAN JOURNAL ON OPERATIONS RESEARCH
, 1994
"... Due to complexity reasons of realistic scheduling applications, often iterative improvement techniques that perform a kind of local search to improve a given schedule are proposed instead of enumeration techniques that guarantee optimal solutions. In this paper we describe an experimental compari ..."
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Cited by 27 (9 self)
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Due to complexity reasons of realistic scheduling applications, often iterative improvement techniques that perform a kind of local search to improve a given schedule are proposed instead of enumeration techniques that guarantee optimal solutions. In this paper we describe an experimental
Theoretical improvements in algorithmic efficiency for network flow problems

, 1972
"... This paper presents new algorithms for the maximum flow problem, the Hitchcock transportation problem, and the general minimumcost flow problem. Upper bounds on ... the numbers of steps in these algorithms are derived, and are shown to compale favorably with upper bounds on the numbers of steps req ..."
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Cited by 565 (0 self)
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This paper presents new algorithms for the maximum flow problem, the Hitchcock transportation problem, and the general minimumcost flow problem. Upper bounds on ... the numbers of steps in these algorithms are derived, and are shown to compale favorably with upper bounds on the numbers of steps required by earlier algorithms. First, the paper states the maximum flow problem, gives the FordFulkerson labeling method for its solution, and points out that an improper choice of flow augmenting paths can lead to severe computational difficulties. Then rules of choice that avoid these difficulties are given. We show that, if each flow augmentation is made along an augmenting path having a minimum number of arcs, then a maximum flow in an nnode network will be obtained after no more than ~(n a n) augmentations; and then we show that if each flow change is chosen to produce a maximum increase in the flow value then, provided the capacities are integral, a maximum flow will be determined within at most 1 + logM/(M1) if(t, S) augmentations, wheref*(t, s) is the value of the maximum flow and M is the maximum number of arcs across a cut. Next a new algorithm is given for the minimumcost flow problem, in which all shortestpath computations are performed on networks with all weights nonnegative. In particular, this
2005 MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res
"... The accuracy of multiple sequence alignment program MAFFT has been improved. The new version (5.3) of MAFFT offers new iterative refinement options, HINSi, FINSi and GINSi, in which pairwise alignment information are incorporated into objective function. These new options of MAFFT showed hig ..."
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Cited by 788 (5 self)
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The accuracy of multiple sequence alignment program MAFFT has been improved. The new version (5.3) of MAFFT offers new iterative refinement options, HINSi, FINSi and GINSi, in which pairwise alignment information are incorporated into objective function. These new options of MAFFT showed
Trace Scheduling: A Technique for Global Microcode Compaction
 IEEE TRANSACTIONS ON COMPUTERS
, 1981
"... Microcode compaction is the conversion of sequential microcode into efficient parallel (horizontal) microcode. Local compaction techniques are those whose domain is basic blocks of code, while global methods attack code with a general flow control. Compilation of highlevel microcode languages int ..."
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Cited by 684 (5 self)
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with a broad overview of the program. Important operations are given priority, no matter what their source block was. This is in sharp contrast with earlier methods, which compact one block at a time and then attempt iterative improvement. It is argued that those methods suffer from the lack
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 the solution to a nonlinear programming relaxation. This relaxation can be interpreted both as a semidefinite program and as an eigenvalue minimization problem. The best previously known approximation algorithms for these problems had performance guarantees of ...
Genet: A connectionist architecture for solving constraint satisfaction problems by iterative improvement
 In Proceedings of AAAI'94
, 1994
"... New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima. In th ..."
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Cited by 93 (19 self)
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New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima
An Iterative Improved kmeans Clustering
"... Abstract: Clustering is a data mining (machine learning), unsupervised learning technique used to place data elements into related groups without advance knowledge of the group definitions. One of the most popular and widely studied clustering methods that minimize the clustering error for points in ..."
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in Euclidean space is called Kmeans clustering. However, the kmeans method converges to one of many local minima, and it is known that the final results depend on the initial starting points (means). In this research paper, we have introduced and tested an improved algorithm to start the kmeans with good
Results 11  20
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835,718