Search algorithms

Xem 1-20 trên 185 kết quả Search algorithms
  • We introduce a novel search algorithm for statistical machine translation based on dynamic programming (DP). During the search process two statistical knowledge sources are combined: a translation model and a bigram language model. This search algorithm expands hypotheses along the positions of the target string while guaranteeing progressive coverage of the words in the source string. We present experimental results on the Verbmobil task.

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  • Search algorithms aim to find solutions or objects with specified properties and constraints in a large solution search space or among a collection of objects. A solution can be a set of value assignments to variables that will satisfy the constraints or a sub-structure of a given discrete structure. In addition, there are search algorithms, mostly probabilistic, that are designed for the prospective quantum computer.

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  • This paper proposes a new probabilistic algorithm for solving multi-objective optimization problems - Probability-Driven Search Algorithm. The algorithm uses probabilities to control the process in search of Pareto optimal solutions. Especially, we use the absorbing Markov Chain to argue the convergence of the algorithm. Authors test this approach by implementing the algorithm on some benchmark multi-objective optimization problems, and find very good and stable results.

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  • Existing algorithms for generating referential descriptions to sets of objects have serious deficits: while incremental approaches may produce ambiguous and redundant expressions, exhaustive searches are computationally expensive. Mediating between these extreme control regimes, we propose a best-first searching algorithm for uniquely identifying sets of objects. We incorporate linguistically motivated preferences and several techniques to cut down the search space. Preliminary results show the effectiveness of the new algorithm. ...

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  • Outline: Problem-solving agents, Problem types, Problem formulation, Example problems, Basic search algorithms. Replace letters by numbers from 0 to 9 such as no different letter is replaced by the same number and satisfying the following constraint.

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  • Local search is a family of general-purpose techniques for search and optimization problems. Chapter 5: Local Search provides about Local search basics; General local search algorithm; Hill-climbing; Tabu search; Simulated Annealing; WSAT; Conclusions

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Fast LSF Search Algorithm Based on Interframe

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Quasi-Cyclic LDPC Codes of Column-Weight Two Using a Search Algorithm

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Flexible Triangle Search Algorithm for Block-Based Motion Estimation

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  • Abstract-like text summarisation requires a means of producing novel summary sentences. In order to improve the grammaticality of the generated sentence, we model a global (sentence) level syntactic structure. We couch statistical sentence generation as a spanning tree problem in order to search for the best dependency tree spanning a set of chosen words. We also introduce a new search algorithm for this task that models argument satisfaction to improve the linguistic validity of the generated tree. ...

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  • In this paper, we describe a Dynamic Programming (DP) based search algorithm for statistical translation and present experimental results. The statistical translation uses two sources of information: a translation model and a language model. The language model used is a standard bigram model. For the translation lnodel, the alignment probabilities are made dependent on the differences in the alignment positions rather than on the absolute positions.

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  • We present a simple yet powerful hierarchical search algorithm for automatic word alignment. Our algorithm induces a forest of alignments from which we can efficiently extract a ranked k-best list. We score a given alignment within the forest with a flexible, linear discriminative model incorporating hundreds of features, and trained on a relatively small amount of annotated data. We report results on Arabic-English word alignment and translation tasks. Our model outperforms a GIZA++ Model-4 baseline by 6.3 points in F-measure, yielding a 1.

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  • This paper extends previous work on extracting parallel sentence pairs from comparable data (Munteanu and Marcu, 2005). For a given source sentence S, a maximum entropy (ME) classifier is applied to a large set of candidate target translations . A beam-search algorithm is used to abandon target sentences as non-parallel early on during classification if they fall outside the beam. This way, our novel algorithm avoids any document-level prefiltering step.

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  • Various kinds of scored dependency graphs are proposed as packed shared data structures in combination with optimum dependency tree search algorithms. This paper classifies the scored dependency graphs and discusses the specific features of the “Dependency Forest” (DF) which is the packed shared data structure adopted in the “Preference Dependency Grammar” (PDG), and proposes the “Graph Branch Algorithm” for computing the optimum dependency tree from a DF. This paper also reports the experiment showing the computational amount and behavior of the graph branch algorithm. ...

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  • This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model (Roark, 2001a), and experimental results show that it gives competitive performance to the generative model on parsing the Penn treebank. We demonstrate that training a perceptron model to combine with the generative model during search provides a 2.

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  • In chapter 11, we cover searching and sorting. After you have read and studied this chapter, you should be able to: Perform linear and binary search algorithms on small arrays, determine whether a linear or binary search is more effective for a given situation, perform selection and bubble sort algorithms, describe the heapsort algorithm and show how its performance is superior to the other two algorithms, apply basic sorting algorithms to sort an array of objects.

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  • Chapter 11 - Sorting and searching. In this chapter, we will present a better searching algorithm called binary search. To apply binary search, an array must be sorted. Sorting is a technique to arrange elements in some order, and it is one of the fundamental operations we study in computer science.

    ppt58p tangtuy11 20-05-2016 3 1   Download

  • In chapter 16 you will learn: To search for a given value in an array using linear search and binary search, to sort arrays using the iterative selection and insertion sort algorithms, to sort arrays using the recursive merge sort algorithm, to determine the efficiency of searching and sorting algorithms, to use loop invariants to help ensure the correctness of your programs.

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  • This book is to examine the most important algorithms in use on today's computers and to teach the basic techniques with the increasing number who was interested in computer users becoming increasingly serious. It is appropriate for use as a textbook for a course Monday, Tuesday or Wednesday in the computer Science: After students have had some programming skills and familiarity computer system, but before they have advanced specialized courses field of computer science or computer applications.

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  • Algorithms play the central role both in the science and practice of computing. Recognition of this fact has led to the appearance of a considerable number of textbooks on the subject. By and large, they follow one of two alternatives in presenting algorithms. One classifies algorithms according to a problem type. Such a book would have separate chapters on algorithms for sorting, searching, graphs, and so on. The advantage of this approach is that it allows an immediate comparison of, say, the efficiency of different algorithms for the same problem.

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