Probabilistic Proximity Search Algorithms Based on Compact Partitions

Zitieren

Dateien zu dieser Ressource

Prüfsumme: MD5:74651200bc859ea425a042f519628258

BUSTOS CÁRDENAS, Benjamin Eugenio, Gonzalo NAVARRO, 2004. Probabilistic Proximity Search Algorithms Based on Compact Partitions. In: Journal of discrete algorithms. 2(1), pp. 115-134

@article{Bustos Cardenas2004Proba-5539, title={Probabilistic Proximity Search Algorithms Based on Compact Partitions}, year={2004}, doi={10.1016/S1570-8667(03)00067-4}, number={1}, volume={2}, journal={Journal of discrete algorithms}, pages={115--134}, author={Bustos Cárdenas, Benjamin Eugenio and Navarro, Gonzalo} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/5539"> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:56:18Z</dc:date> <dc:creator>Navarro, Gonzalo</dc:creator> <dcterms:bibliographicCitation>First publ. in: Journal of discrete algorithms 2 (2004), 1, pp. 115-134</dcterms:bibliographicCitation> <dc:creator>Bustos Cárdenas, Benjamin Eugenio</dc:creator> <dc:format>application/pdf</dc:format> <dcterms:title>Probabilistic Proximity Search Algorithms Based on Compact Partitions</dcterms:title> <dcterms:rights rdf:resource="https://creativecommons.org/licenses/by-nc-nd/2.0/legalcode"/> <dc:contributor>Bustos Cárdenas, Benjamin Eugenio</dc:contributor> <dcterms:issued>2004</dcterms:issued> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5539"/> <dc:rights>deposit-license</dc:rights> <dc:contributor>Navarro, Gonzalo</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:56:18Z</dcterms:available> <dcterms:abstract xml:lang="eng">The main bottleneck of the research in metric space searching is the so-called curse of dimensionality, which makes the task of searching some metric spaces intrinsically difficult, whatever algorithm is used. A recent trend to break this bottleneck resorts to probabilistic algorithms, where it has been shown that one can find 99% of the relevant objects at a fraction of the cost of the exact algorithm. These algorithms are welcome in most applications because resorting to metric space searching already involves a fuzziness in the retrieval requirements. In this paper, we push further in this direction by developing probabilistic algorithms on data structures whose exact versions are the best for high dimensions. As a result, we obtain probabilistic algorithms that are better than the previous ones. We give new insights on the problem and propose a novel view based on time-bounded searching. We also propose an experimental framework for probabilistic algorithms that permits comparing them in offline mode.</dcterms:abstract> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

BN04Bustos.pdf 105

Das Dokument erscheint in:

deposit-license Solange nicht anders angezeigt, wird die Lizenz wie folgt beschrieben: deposit-license

KOPS Suche


Stöbern

Mein Benutzerkonto