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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Shakhnarovich, Gregory, Darrell, Trevor, Indyk, Piotr
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©2005.
Colección:Neural information processing series.
Temas:
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.