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...
Clasificación: | Libro Electrónico |
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Otros Autores: | , , |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Cambridge, Mass. :
MIT Press,
©2005.
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Colección: | Neural information processing series.
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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.