Relevant bibliographies by topics / Hierarchical search

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Hierarchical search'

Author: Grafiati
Published: 23 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Hierarchical search"

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KOREPIN, VLADIMIR E., and YING XU. "HIERARCHICAL QUANTUM SEARCH." International Journal of Modern Physics B 21, no. 31 (December 20, 2007): 5187–205. http://dx.doi.org/10.1142/s0217979207038344.

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Database search has wide applications and is used as a subroutine in many important algorithms. In this paper, we will consider a database with a single target item. Quantum algorithm (Grover) locates the target item faster than any classical algorithm. In addition to a full (Grover) search, it frequently occurs that one is looking for a group of items (a block) containing the target item, rather than the target item itself. This problem is known as partial search. As a generalization of the full search, partial search is of particular importance in practice. Partial search trades accuracy for speed, i.e., it works faster than a full search. There exists different versions of partial search. We will study the optimized version of the algorithm discovered by Grover and Radhakrishnan and call it GRK. GRK can be applied successively (in a sequence). First, the database is partitioned into blocks and GRK is applied to find the target block. This target block is then partitioned into subblocks and GRK is used again to find the target subblock. This procedure can be repeated if the database is large enough. (This sequence of GRK's is called a hierarchy.) Another possibility is to partition the database into subblocks directly and use GRK to find the target subblock once. In this paper, we will prove that the latter is faster (makes less queries to the Oracle).
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Zupan, Jure, and Morton E. Munk. "Feedback search of hierarchical trees." Analytical Chemistry 58, no. 14 (December 1986): 3219–25. http://dx.doi.org/10.1021/ac00127a065.

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de Buy Wenniger, Gideon Maillette, and Khalil Sima’an. "Visualization, Search and Analysis of Hierarchical Translation Equivalence in Machine Translation Data." Prague Bulletin of Mathematical Linguistics 101, no. 1 (April 1, 2014): 43–54. http://dx.doi.org/10.2478/pralin-2014-0003.

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Abstract Translation equivalence constitutes the basis of all Machine Translation systems including the recent hierarchical and syntax-based systems. For hierarchical MT research it is important to have a tool that supports the qualitative and quantitative analysis of hierarchical translation equivalence relations extracted from word alignments in data. In this paper we present such a toolkit and exemplify some of its uses. The main challenges taken up in designing this tool are the efficient and compact, yet complete, representation of hierarchical translation equivalence coupled with an intuitive visualization of these hierarchical relations. We exploit a new hierarchical representation, called Hierarchical Alignment Trees (HATs), which is based on an extension of the algorithms used for factorizing n-ary branching SCFG rules into their minimally-branching equivalents. Our toolkit further provides a search capability based on hierarchically relevant properties of word alignments and/or translation equivalence relations. Finally, the tool allows detailed statistical analysis of word alignments, thereby providing a breakdown of alignment statistics according to the complexity of translation equivalence units or reordering phenomena. We illustrate this with an empirical study of the coverage of inversion-transduction grammars for a number of corpora enriched with manual or automatic word alignments, followed by a breakdown of corpus statistics to reordering complexity.
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Chen, Lin Chih. "Building a Post-Search Academic Search Engine Based on a Serial of Clustering Methods." Applied Mechanics and Materials 284-287 (January 2013): 3051–55. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3051.

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Academic search engines, such as Google Scholar and Scirus, provide a Web-based interface to effectively find relevant scientific articles to researchers. However, current academic search engines are lacking the ability to cluster the search results into a hierarchical tree structure. In this paper, we develop a post-search academic search engine by using a mixed clustering method. In this method, we first adopt a suffix tree clustering and a two-way hash mechanism to generate all meaningful labels. We then develop a divisive hierarchical clustering algorithm to organize the labels into a hierarchical tree. According to the results of experiments, we conclude that using our mixed clustering method to cluster the search results can give significant performance gains than current academic search engines. In this paper, we make two contributions. First, we present a high performance academic search engine based on our mixed clustering method. Second, we develop a divisive hierarchical clustering algorithm to organize all returned search results into a hierarchical tree structure.
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Zhang, Qiong. "Hierarchical Route Representation, Indexing, and Search." IEEE Pervasive Computing 7, no. 2 (April 2008): 78–84. http://dx.doi.org/10.1109/mprv.2008.31.

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del Campo, Jorge M., and Andreas M. Köster. "A hierarchical transition state search algorithm." Journal of Chemical Physics 129, no. 2 (July 14, 2008): 024107. http://dx.doi.org/10.1063/1.2950083.

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Chen, Di, Shanshan Zhang, Wanli Ouyang, Jian Yang, and Bernt Schiele. "Hierarchical Online Instance Matching for Person Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10518–25. http://dx.doi.org/10.1609/aaai.v34i07.6623.

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Person Search is a challenging task which requires to retrieve a person's image and the corresponding position from an image dataset. It consists of two sub-tasks: pedestrian detection and person re-identification (re-ID). One of the key challenges is to properly combine the two sub-tasks into a unified framework. Existing works usually adopt a straightforward strategy by concatenating a detector and a re-ID model directly, either into an integrated model or into separated models. We argue that simply concatenating detection and re-ID is a sub-optimal solution, and we propose a Hierarchical Online Instance Matching (HOIM) loss which exploits the hierarchical relationship between detection and re-ID to guide the learning of our network. Our novel HOIM loss function harmonizes the objectives of the two sub-tasks and encourages better feature learning. In addition, we improve the loss update policy by introducing Selective Memory Refreshment (SMR) for unlabeled persons, which takes advantage of the potential discrimination power of unlabeled data. From the experiments on two standard person search benchmarks, i.e. CUHK-SYSU and PRW, we achieve state-of-the-art performance, which justifies the effectiveness of our proposed HOIM loss on learning robust features.
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Gies, D. R., S. J. Williams, R. A. Matson, Z. Guo, S. M. Thomas, J. A. Orosz, and G. J. Peters. "A SEARCH FOR HIERARCHICAL TRIPLES USINGKEPLERECLIPSE TIMING." Astronomical Journal 143, no. 6 (May 2, 2012): 137. http://dx.doi.org/10.1088/0004-6256/143/6/137.

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NAGARAJ SHENOY, U., Y. N. SRIKANT, V. P. BHATKAR, and SANDEEP KOHLI. "AUTOMATIC DATA PARTITIONING BY HIERARCHICAL GENETIC SEARCH." Parallel Algorithms and Applications 14, no. 2 (July 1999): 119–47. http://dx.doi.org/10.1080/10637199808947382.

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Tedmori, S., and N. Al-Najdawi. "Hierarchical stochastic fast search motion estimation algorithm." IET Computer Vision 6, no. 1 (2012): 21. http://dx.doi.org/10.1049/iet-cvi.2010.0188.

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Dissertations / Theses on the topic "Hierarchical search"

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Xu, Xin. "Interactive hierarchical generate and test search." Thesis, University of Ottawa (Canada), 1991. http://hdl.handle.net/10393/7934.

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Most of the search methods used in AI are inflexible. Interactive search is a new kind of search in which the search system can communicate and cooperate with external agents. There are two kinds of agents: human agents and non-human agents. Through interaction with human agents (man-machine interaction), the search system can make use of the human talent of judging the quality of a solution. Through interaction with non-human agents (machine-machine interaction), the search system can automatically exploit knowledge from its environment. An interactive search system has the ability to take advice from external agents. The ordinary non-interactive search models are the special instances of interactive search when the advice sequences are empty. We are investigating a particular kind of Interactive Search, IHGT (Interactive Hierarchical Generate and Test) search, which is established by introducing interactive ability into HGT (Hierarchical Generate and Test) search. To make HGT search interactive, we created an editor called GE (Generator Editor). GE was implemented in Prolog. GE is a bottom level language shell outside the HGT search model which translates advice into dynamic changes of all the three search factors. (Abstract shortened by UMI.)
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Shenoy, U. Nagaraj. "Automatic Data Partitioning By Hierarchical Genetic Search." Thesis, Indian Institute of Science, 1996. http://hdl.handle.net/2005/172.

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CDAC
The introduction of languages like High Performance Fortran (HPF) which allow the programmer to indicate how the arrays used in the program have to be distributed across the local memories of a multi-computer has not completely unburdened the parallel programmer from the intricacies of these architectures. In order to tap the full potential of these architectures, the compiler has to perform this crucial task of data partitioning automatically. This would not only unburden the programmer but would make the programs more efficient since the compiler can be made more intelligent to take care of the architectural nuances. The topic of this thesis namely the automatic data partitioning deals with finding the best data partition for the various arrays used in the entire program in such a way that the cost of execution of the entire program is minimized. The compiler could resort to runtime redistribution of the arrays at various points in the program if found profitable. Several aspects of this problem have been proven to be NP-complete. Other researchers have suggested heuristic solutions to solve this problem. In this thesis we propose a genetic algorithm namely the Hierarchical Genetic Search algorithm to solve this problem.
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Normore, Lorraine Dombrowski. "Strategies in searching hierarchical data structures /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487265143147771.

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Manabe, Tomohiro. "Web Search Based on Hierarchical Heading-Block Structure Analysis." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/215681.

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The contents of Section 2.2 and Chapter 4 first appeared in proceedings of the 12th International Conference on Web Information Systems and Technologies, 2016 (www.webist.org). The contents of Section 2.3 and Chapter 5 first appeared in DBSJ Journal, vol. 14, article no. 2, March 2016. The contents of Section 2.5 and Chapter 7 first appeared in proceedings of the 11th Asia Information Retrieval Societies Conference, Lecture Notes in Computer Science, vol. 9460, pp. 188-200, 2015 (The final publication is available at link.springer.com).
Kyoto University (京都大学)
0048
新制・課程博士
博士(情報学)
甲第19854号
情博第605号
新制||情||105(附属図書館)
32890
京都大学大学院情報学研究科社会情報学専攻
(主査)教授 田島 敬史, 教授 田中 克己, 教授 吉川 正俊
学位規則第4条第1項該当
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Dickinson, Anthony R. "Hierarchical organisation in serial search tasks by Cebus apella monkeys." Thesis, University of Edinburgh, 1998. http://hdl.handle.net/1842/21198.

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The thesis reports the first demonstration of two-level hierarchical and linear organisation in six monkeys (Cebus apella). In behaviours elaborated over a four-year period, the hypothesis tested (McGonigle and Chalmers, 1992) was that progressive increases in task difficulty would be compensated by data reducing, economic, organisational structures. Novel touch-screen based procedures required the seriation and search of each item in a test set which, when increased in size, lead to geometrical increases in difficulty. Whilst on one scenario the subject should begin to fail as the task increases in difficulty, the performance of all subjects has shown progressive adaptation to such task requirements. This indicates an underlying dynamic process consistent with the operating hypothesis that cognitive organisation, both linear and hierarchical, are emergent responses motivated by a need for cognitive economy.
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Roshani, Asra. "Unsupervised segmentation of sequences using harmony search and hierarchical clustering techniques." Master's thesis, Université Laval, 2014. http://hdl.handle.net/20.500.11794/25350.

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Dans le contexte de traitement automatique du langage naturel, les données le plus souvent sont présentées comme une longue séquence de caractères discrets. Donc, l'identification d'un modèle intéressant dans la longue séquence peut être une tâche difficile. En conséquence, la segmentation automatique de données serait extrêmement utile pour extraire les sous-séquences et les morceaux significatifs à partir d'une longue séquence de données. La segmentation de données est l'une des étapes de prétraitement les plus importantes dans plusieurs tâches de traitement du langage naturel. La segmentation de mots est considérée comme la tâche de trouver des morceaux significatifs dans le corpus de textes. L'objectif principal de cette étude est de présenter une technique de segmentation hiérarchique non supervisée en utilisant l'algorithme de recherche d'harmonie (Harmony Search algorithm) qui est une approche d'optimisation méta-heuristique. Dans la technique proposée, la tâche de segmentation de mots est réalisée à l'aide d'une recherche d'harmonie binaire (Binary Harmony search) qui une forme particulière de l'algorithme de recherche d'harmonie. La construction et la formation de modèles de langue sont accomplies par un lexique hiérarchique et un algorithme de Baum-Welch. De plus, pour améliorer la performance et la convergence de la recherche de l'harmonie binaire, quelques modifications innovantes sont appliquées. En général, cette étude présente un algorithme de segmentation de mots hiérarchique non supervisée basée sur une méthode recherche de l'harmonie et examine toutes les questions relatives y compris: la segmentation de mots représentées en format binaire, l'harmonie binaire, l'amélioration de la procédure de l'ajustement du lancement, la définition de la fonction objective en recherche d'harmonie et la politique de pénalité. La performance de l'algorithme est évaluée selon la précision de la segmentation, le rappel, la F-mesure et le temps d'exécution de l'algorithme. Une partie du corpus Moby Dick est utilisée comme étude de cas. Nos expérimentations montrent que l'approche de segmentation basée sur une recherche d'harmonie fournit plusieurs de bons segments, mais qu'il nécessite un long temps d'exécution.
In the context of natural language processing, data is presented most of the time as a long sequence of discrete characters. Therefore, identifying interesting patterns within the long sequence can be a challenging task. Consequently, automatic segmentation of data would be extremely useful to extract the meaningful sub-sequences and chunks from a long data sequence. Segmentation of raw data is one of the most important preprocessing steps in many natural language processing tasks. Word segmentation is considered as the task of finding meaningful chunks, i.e. words, within a text corpus. The main objective of this study is to present an unsupervised hierarchical segmentation technique using Harmony Search algorithm which is a meta-heuristic optimization approach. In the proposed technique, the word segmentation task is performed using a Binary Harmony Search (a special form of Harmony Search). The language model construction and training are accomplished using a hierarchical lexicon and Baum-welch algorithm. Moreover, to improve the performance and convergence of the Binary Harmony Search, some innovative modifications are applied. In general, this study introduces an unsupervised hierarchical word segmentation algorithm based on Harmony Search approach and investigates the following related issues: word segmentation mapping to binary format, Binary Harmony Search, pitch adjustment procedure improvement, Harmony Search objective function definition, and penalty policy. The performance of the algorithm is valuated using segmentation precision, recall, F-measure and the algorithm run time when applied to the part of famous Moby Dick story as the case study. Our experiments reveal that the segmentation approach based on Harmony Search provides significantly good segments, while it requires significant run time.
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Anguelov, Bobby. "Video game pathfinding and improvements to discrete search on grid-based maps." Diss., University of Pretoria, 2011. http://hdl.handle.net/2263/22940.

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The most basic requirement for any computer controlled game agent in a video game is to be able to successfully navigate the game environment. Pathfinding is an essential component of any agent navigation system. Pathfinding is, at the simplest level, a search technique for finding a route between two points in an environment. The real-time multi-agent nature of video games places extremely tight constraints on the pathfinding problem. This study aims to provide the first complete review of the current state of video game pathfinding both in regards to the graph search algorithms employed as well as the implications of pathfinding within dynamic game environments. Furthermore this thesis presents novel work in the form of a domain specific search algorithm for use on grid-based game maps: the spatial grid A* algorithm which is shown to offer significant improvements over A* within the intended domain. Copyright
Dissertation (MSc)--University of Pretoria, 2011.
Computer Science
unrestricted
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Zheng, Li. "Towards Next Generation Vertical Search Engines." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1517.

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As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results. In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.
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Fang, Qijun. "Model search strategy when P >> N in Bayesian hierarchical setting." View electronic thesis (PDF), 2009. http://dl.uncw.edu/etd/2009-2/fangq/qijunfang.pdf.

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Salomatin, Konstantin. "Large-scale hierarchical optimization for online advertising and wind farm planning." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/337.

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This thesis develops a framework to investigate and design novel optimization methods for two important problems: computational advertising (particularly, sponsored search) and wind farm turbine-layout planning. Whereas very different in specifics, both problems share some common abstractions. The existing solution in sponsored search is based on a greedy pay-per-click auction and is suitable only for advertisers seeking a direct response. It does not apply to advertisers who target certain numbers of clicks in a predefined time period. To address this new challenge, we introduce a unified optimization framework combining pay-per-click auctions and guaranteed delivery in sponsored search. Our new method maximizes the revenue of the search engine, targets a guaranteed number of ad clicks per campaign for advertisers willing to pay a premium, and enables keyword auctions for all others. Results combining revenue to the search engine and click rates for the advertisers show superior performance over strong baselines. The proposed framework is based on linear programming with delayed column generation for computational tractability at scale. We design a game theoretic approach to optimize the strategy for individual advertisers, i.e. to optimize their choices between auctions and guaranteed delivery, and analyze the behavior of the new market formed by our framework. Specifically, we introduce a new method for computing the approximate Nash equilibrium where an exact computation would prove computationally intractable. We rely on approximations of complex utility functions, a combination of simulated annealing and integer linear programming as our principled approach. Wind farm layout optimization is the selection of optimal locations for placement of large wind turbines taking into account factors such as topographical features, prevalent but non-constant wind direction and turbine-wake interference. Existing approaches are deficient in their inability to consider long distance turbine interference, changing wind speed and direction and multiple types of wind turbines in optimization. The dissertation develops an optimization framework based on a scalable divided-and-conquer strategy that enables scalability to real-world wind farm scales taking into account the aforementioned complexities in the optimization process. Essentially the process optimizes in a hierarchical manner at different levels of granularity. This hierarchical decomposition approach to optimization is common to both search-advertisement and wind-farm layout challenges.
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Books on the topic "Hierarchical search"

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Heinemann, Frédéric. BSP Extensions: how to master Web reporting with HTMLB: How to use undocumented HTMLB elements ; Web application development to manage code fragments. hierarchical navigation, table selection, detail display, comprehensive search templates, and much more. Fort Lee, NJ [u.a.]: Galileo Press, 2005.

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Newman, Mark. Network search. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0018.

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This chapter gives a discussion of search processes on networks. It begins with a discussion of web search, including crawlers and web ranking algorithms such as PageRank. Search in distributed databases such as peer-to-peer networks is also discussed, including simple breadth-first search style algorithms and more advanced “supernode” approaches. Finally, network navigation is discussed at some length, motivated by consideration of Milgram's letter passing experiment. Kleinberg's variant of the small-world model is introduced and it is shown that efficient navigation is possible only for certain values of the model parameters. Similar results are also derived for the hierarchical model of Watts et al.
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Voorhees, E. The effectiveness and efficiency of agglomerative hierarchic clustering in document retrieval. 1989.

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The effectiveness and efficiency of agglomerative hierarchic clustering in document retrieval. Ann Arbor, Mich: University Microfilms International, 1986.

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Kaplan, Jonathan, and Federico Paredes Umaña. Water, Cacao, and The Early Maya of Chocóla. University Press of Florida, 2018. http://dx.doi.org/10.5744/florida/9780813056746.001.0001.

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Before the authors’ research, Chocolá was no more than an intriguing legend. Chocolá’s apparent political links to the greatest Preclassic southern Maya area polity, Kaminaljuyu, would make any discovery about Chocolá conceivably vital to a better understanding of Maya origins and New World archaeology, as both ancient cities are located in the Southern Maya Region. Two facts led researchers to search more specifically for the material bases for Chocolá’s rise to power: 1) Mesoamerica’s greatest rainfall, 2) cacao groves around the modern village lying atop the ancient city. Cacao was so important to the Maya that, mythologically, the cacao god was the maize god’s brother and uncle of the “Hero Twins,” conceived as the aboriginal creators of the Maya people. If water control systems have been documented archaeologically at virtually all great ancient cities around the world, cacao is uniquely a Maya “invention,” the Maya being the first people in the world to domesticate the plant and cultivate it through intensive agriculture. These two discoveries—impressive water management and cacao at Preclassic Chocolá—likely are not coincidental. A complex, hierarchical society would have been in place for arboriculture of water-thirsty cacao for long-distance ancient trade. Thus, two material substances, one necessary for human survival, the other highly valued throughout Mesoamerica as consumable and essential in Maya mythology, may explain, in part, how this and other Southern Maya “kingdoms of chocolate” may represent a “sweet beginning” for one of the greatest civilizations of the ancient world.
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Book chapters on the topic "Hierarchical search"

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Michaelsen, Eckart, and Jochen Meidow. "Search." In Hierarchical Perceptual Grouping for Object Recognition, 101–6. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04040-6_6.

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Fernández, Juan A., and Javier González. "Hierarchical Path Search." In Multi-Hierarchical Representation of Large-Scale Space, 71–113. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9666-4_4.

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Holte, Robert C., Jeffery Grajkowski, and Brian Tanner. "Hierarchical Heuristic Search Revisited." In Lecture Notes in Computer Science, 121–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527862_9.

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Baruch, Gilad, Shmuel Tomi Klein, and Dana Shapira. "Applying Compression to Hierarchical Clustering." In Similarity Search and Applications, 151–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02224-2_12.

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Hall, Mathew. "Complexity Metrics for Hierarchical State Machines." In Search Based Software Engineering, 76–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23716-4_10.

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Sahana, Sudipta, Rajesh Bose, and Debabrata Sarddar. "Fog-Based Hierarchical Search Optimization." In Social Transformation – Digital Way, 674–81. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1343-1_55.

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Cigarran, Juan M., Anselmo Pen̈as, Julio Gonzalo, and Felisa Verdejo. "Evaluating Hierarchical Clustering of Search Results." In String Processing and Information Retrieval, 49–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11575832_7.

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Lokoč, Jakub, Přemysl Čech, Jiří Novák, and Tomáš Skopal. "Cut-Region: A Compact Building Block for Hierarchical Metric Indexing." In Similarity Search and Applications, 85–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32153-5_7.

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Gomez, Juan Carlos, and Marie-Francine Moens. "A Survey of Automated Hierarchical Classification of Patents." In Professional Search in the Modern World, 215–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12511-4_11.

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Zheng, Hai-Tao, Zhuren Wang, and Xi Xiao. "A Learning Approach to Hierarchical Search Result Diversification." In Web and Big Data, 303–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63564-4_25.

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Conference papers on the topic "Hierarchical search"

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KOREPIN, VLADIMIR E., and YING XU. "HIERARCHICAL QUANTUM SEARCH." In Statistical Physics, High Energy, Condensed Matter and Mathematical Physics - The Conference in Honor of C. N. Yang'S 85th Birthday. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812794185_0039.

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Pauls, Adam, and Dan Klein. "Hierarchical search for parsing." In Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1620754.1620835.

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Yiu, Ying Fung, and Rabi Mahapatra. "Hierarchical Evolutionary Heuristic A* Search." In 2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI). IEEE, 2020. http://dx.doi.org/10.1109/hccai49649.2020.00011.

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Horn, Daniel, Jörg Stork, Nils-Jannik Schüßler, and Martin Zaefferer. "Surrogates for hierarchical search spaces." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3321707.3321765.

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Guorong Xu, Changjun Wu, and Xiaoli Du. "Sentences, Hierarchical Clustering for Shopping Search." In Second International Conference on Semantics, Knowledge, and Grid (SKG 2006). IEEE, 2006. http://dx.doi.org/10.1109/skg.2006.91.

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de Lima Marquezino, Franklin, Renato Portugal, and Stefan Boettcher. "Quantum search algorithms on hierarchical networks." In 2011 IEEE Information Theory Workshop (ITW). IEEE, 2011. http://dx.doi.org/10.1109/itw.2011.6089429.

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Denisa, Miha, Bojan Nemec, and Ales Ude. "Cooperative movements through hierarchical database search." In 2017 18th International Conference on Advanced Robotics (ICAR). IEEE, 2017. http://dx.doi.org/10.1109/icar.2017.8023494.

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Gurram, Mohana M., and Christopher D. Knight. "Functional Hierarchical Search Results Data Analysis." In 2008 IEEE Aerospace Conference. IEEE, 2008. http://dx.doi.org/10.1109/aero.2008.4526583.

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Berggren, Fredrik, and Branislav M. Popovic. "A Non-Hierarchical Cell Search Scheme." In 2007 IEEE Wireless Communications and Networking Conference. IEEE, 2007. http://dx.doi.org/10.1109/wcnc.2007.430.

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Strens, Malcolm. "Efficient hierarchical MCMC for policy search." In Twenty-first international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1015330.1015381.

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