Nearest-neighbor methods in learning and vision :: theory and practice /
Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these m...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2005.
|
| Schriftenreihe: | Neural information processing series.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications. The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naive methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks. |
| Beschreibung: | " ... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Preface |
| Beschreibung: | 1 online resource (vi, 252 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780262256957 0262256959 1282096753 9781282096752 9786612096754 6612096756 1423772539 9781423772538 |
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| 500 | |a " ... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Preface | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Nearest-neighbor searching and metric space dimensions / Kenneth L. Clarkson -- Locality-sensitive hashing using stable distributions / Alexandr Andoni [and others] -- New algorithms for efficient high-dimensional nonparametric classification / Ting Liu, Andrew W. Moore, and Alexander Gray -- Approximate nearest neighbor regression in very high dimensions / Sethu Vijayakumar, Aaron D'Souza, and Stefan Schaal -- Learning embeddings for fast approximate nearest neighbor retrieval / Vassilis Athitsos [and others] -- Parameter-sensitive hashing for fast pose estimation / Gregory Shakhnarovich, Paul Viola, and Trevor Darrell -- Contour matching using approximate Earth mover's distance / Kristen Grauman and Trevor Darrell -- Adaptive mean shift based clustering in high dimensions / Ilan Shimshoni, Bogdan Georgescu, and Peter Meer -- Object recognition using locality sensitive hashing of shape contexts / Andrea Frome and Jitendra Malik. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications. The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naive methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Nearest neighbor analysis (Statistics) |v Congresses. | |
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| contents | Nearest-neighbor searching and metric space dimensions / Kenneth L. Clarkson -- Locality-sensitive hashing using stable distributions / Alexandr Andoni [and others] -- New algorithms for efficient high-dimensional nonparametric classification / Ting Liu, Andrew W. Moore, and Alexander Gray -- Approximate nearest neighbor regression in very high dimensions / Sethu Vijayakumar, Aaron D'Souza, and Stefan Schaal -- Learning embeddings for fast approximate nearest neighbor retrieval / Vassilis Athitsos [and others] -- Parameter-sensitive hashing for fast pose estimation / Gregory Shakhnarovich, Paul Viola, and Trevor Darrell -- Contour matching using approximate Earth mover's distance / Kristen Grauman and Trevor Darrell -- Adaptive mean shift based clustering in high dimensions / Ilan Shimshoni, Bogdan Georgescu, and Peter Meer -- Object recognition using locality sensitive hashing of shape contexts / Andrea Frome and Jitendra Malik. |
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| indexdate | 2024-11-27T13:15:52Z |
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| isbn | 9780262256957 0262256959 1282096753 9781282096752 9786612096754 6612096756 1423772539 9781423772538 |
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| series | Neural information processing series. |
| series2 | Neural information processing series |
| spelling | Nearest-neighbor methods in learning and vision : theory and practice / edited by Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk. Cambridge, Mass. : MIT Press, ©2005. 1 online resource (vi, 252 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Neural information processing series " ... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Preface Includes bibliographical references and index. Nearest-neighbor searching and metric space dimensions / Kenneth L. Clarkson -- Locality-sensitive hashing using stable distributions / Alexandr Andoni [and others] -- New algorithms for efficient high-dimensional nonparametric classification / Ting Liu, Andrew W. Moore, and Alexander Gray -- Approximate nearest neighbor regression in very high dimensions / Sethu Vijayakumar, Aaron D'Souza, and Stefan Schaal -- Learning embeddings for fast approximate nearest neighbor retrieval / Vassilis Athitsos [and others] -- Parameter-sensitive hashing for fast pose estimation / Gregory Shakhnarovich, Paul Viola, and Trevor Darrell -- Contour matching using approximate Earth mover's distance / Kristen Grauman and Trevor Darrell -- Adaptive mean shift based clustering in high dimensions / Ilan Shimshoni, Bogdan Georgescu, and Peter Meer -- Object recognition using locality sensitive hashing of shape contexts / Andrea Frome and Jitendra Malik. Print version record. Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications. The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naive methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks. English. Nearest neighbor analysis (Statistics) Congresses. Machine learning Congresses. Algorithms Congresses. Geometry Data processing Congresses. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Algorithms Artificial Intelligence Analyse du plus proche voisin (Statistique) Congrès. Apprentissage automatique Congrès. Algorithmes Congrès. Géométrie Informatique Congrès. Algorithmes. Intelligence artificielle. algorithms. aat artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Algorithms fast Geometry Data processing fast Machine learning fast Nearest neighbor analysis (Statistics) fast Anwendung gnd http://d-nb.info/gnd/4196864-5 Maschinelles Lernen gnd Maschinelles Sehen gnd http://d-nb.info/gnd/4129594-8 Nächste-Nachbarn-Problem gnd http://d-nb.info/gnd/4376579-8 COMPUTER SCIENCE/Machine Learning & Neural Networks Congress https://id.nlm.nih.gov/mesh/D016423 proceedings (reports) aat Conference papers and proceedings fast Conference papers and proceedings. lcgft http://id.loc.gov/authorities/genreForms/gf2014026068 Actes de congrès. rvmgf Shakhnarovich, Gregory. http://id.loc.gov/authorities/names/n2005058267 Darrell, Trevor. http://id.loc.gov/authorities/names/n2005058269 Indyk, Piotr. http://id.loc.gov/authorities/names/n2005058271 has work: Nearest-neighbor methods in learning and vision (Text) https://id.oclc.org/worldcat/entity/E39PCFYmCcwMpfJfVRQVFqg9pd https://id.oclc.org/worldcat/ontology/hasWork Print version: Nearest-neighbor methods in learning and vision. Cambridge, Mass. : MIT Press, ©2005 026219547X (DLC) 2005053124 (OCoLC)61247438 Neural information processing series. http://id.loc.gov/authorities/names/n00009051 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=156924 Volltext |
| spellingShingle | Nearest-neighbor methods in learning and vision : theory and practice / Neural information processing series. Nearest-neighbor searching and metric space dimensions / Kenneth L. Clarkson -- Locality-sensitive hashing using stable distributions / Alexandr Andoni [and others] -- New algorithms for efficient high-dimensional nonparametric classification / Ting Liu, Andrew W. Moore, and Alexander Gray -- Approximate nearest neighbor regression in very high dimensions / Sethu Vijayakumar, Aaron D'Souza, and Stefan Schaal -- Learning embeddings for fast approximate nearest neighbor retrieval / Vassilis Athitsos [and others] -- Parameter-sensitive hashing for fast pose estimation / Gregory Shakhnarovich, Paul Viola, and Trevor Darrell -- Contour matching using approximate Earth mover's distance / Kristen Grauman and Trevor Darrell -- Adaptive mean shift based clustering in high dimensions / Ilan Shimshoni, Bogdan Georgescu, and Peter Meer -- Object recognition using locality sensitive hashing of shape contexts / Andrea Frome and Jitendra Malik. Nearest neighbor analysis (Statistics) Congresses. Machine learning Congresses. Algorithms Congresses. Geometry Data processing Congresses. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Algorithms Artificial Intelligence Analyse du plus proche voisin (Statistique) Congrès. Apprentissage automatique Congrès. Algorithmes Congrès. Géométrie Informatique Congrès. Algorithmes. Intelligence artificielle. algorithms. aat artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Algorithms fast Geometry Data processing fast Machine learning fast Nearest neighbor analysis (Statistics) fast Anwendung gnd http://d-nb.info/gnd/4196864-5 Maschinelles Lernen gnd Maschinelles Sehen gnd http://d-nb.info/gnd/4129594-8 Nächste-Nachbarn-Problem gnd http://d-nb.info/gnd/4376579-8 |
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| title | Nearest-neighbor methods in learning and vision : theory and practice / |
| title_auth | Nearest-neighbor methods in learning and vision : theory and practice / |
| title_exact_search | Nearest-neighbor methods in learning and vision : theory and practice / |
| title_full | Nearest-neighbor methods in learning and vision : theory and practice / edited by Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk. |
| title_fullStr | Nearest-neighbor methods in learning and vision : theory and practice / edited by Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk. |
| title_full_unstemmed | Nearest-neighbor methods in learning and vision : theory and practice / edited by Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk. |
| title_short | Nearest-neighbor methods in learning and vision : |
| title_sort | nearest neighbor methods in learning and vision theory and practice |
| title_sub | theory and practice / |
| topic | Nearest neighbor analysis (Statistics) Congresses. Machine learning Congresses. Algorithms Congresses. Geometry Data processing Congresses. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Algorithms Artificial Intelligence Analyse du plus proche voisin (Statistique) Congrès. Apprentissage automatique Congrès. Algorithmes Congrès. Géométrie Informatique Congrès. Algorithmes. Intelligence artificielle. algorithms. aat artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Algorithms fast Geometry Data processing fast Machine learning fast Nearest neighbor analysis (Statistics) fast Anwendung gnd http://d-nb.info/gnd/4196864-5 Maschinelles Lernen gnd Maschinelles Sehen gnd http://d-nb.info/gnd/4129594-8 Nächste-Nachbarn-Problem gnd http://d-nb.info/gnd/4376579-8 |
| topic_facet | Nearest neighbor analysis (Statistics) Congresses. Machine learning Congresses. Algorithms Congresses. Geometry Data processing Congresses. Artificial intelligence. Algorithms Artificial Intelligence Analyse du plus proche voisin (Statistique) Congrès. Apprentissage automatique Congrès. Algorithmes Congrès. Géométrie Informatique Congrès. Algorithmes. Intelligence artificielle. algorithms. artificial intelligence. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Artificial intelligence Geometry Data processing Machine learning Nearest neighbor analysis (Statistics) Anwendung Maschinelles Lernen Maschinelles Sehen Nächste-Nachbarn-Problem Congress proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. |
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