| dc.contributor.advisor |
Tomaso Poggio |
en_US |
| dc.contributor.author |
Caponnetto, Andrea |
en_US |
| dc.contributor.author |
Poggio, Tomaso |
en_US |
| dc.contributor.author |
Smale, Steve |
en_US |
| dc.contributor.other |
Center for Biological and Computational Learning (CBCL) |
en_US |
| dc.date.accessioned |
2008-06-05T18:00:40Z |
|
| dc.date.available |
2008-06-05T18:00:40Z |
|
| dc.date.issued |
2008-04-04 |
en_US |
| dc.identifier.other |
MIT-CSAIL-TR-2008-030 |
en_US |
| dc.identifier.other |
CBCL-272 |
en_US |
| dc.identifier.uri |
http://hdl.handle.net/1721.1/41858 |
|
| dc.description.abstract |
In this paper we present a class of algorithms for similarity learning on spaces of images. The general framework that we introduce is motivated by some well-known hierarchical pre-processing architectures for object recognition which have been developed during the last decade, and which have been in some cases inspired by functional models of the ventral stream of the visual cortex. These architectures are characterized by the construction of a hierarchy of “local†feature representations of the visual stimulus. We show that our framework includes some well-known techniques, and that it is suitable for the analysis of dynamic visual stimuli, presenting a quantitative error analysis in this setting. |
en_US |
| dc.description.provenance |
Submitted by CSAIL Importer (publications-dspace@csail.mit.edu) on 2008-06-05T18:00:39Z
No. of bitstreams: 2
MIT-CSAIL-TR-2008-030.pdf: 298512 bytes, checksum: fae8321f0b8db99683621a90505d318e (MD5)
MIT-CSAIL-TR-2008-030.ps: 73870 bytes, checksum: 0a9ae6643b57f22cb912b16233de25aa (MD5) |
en |
| dc.description.provenance |
Made available in DSpace on 2008-06-05T18:00:40Z (GMT). No. of bitstreams: 2
MIT-CSAIL-TR-2008-030.pdf: 298512 bytes, checksum: fae8321f0b8db99683621a90505d318e (MD5)
MIT-CSAIL-TR-2008-030.ps: 73870 bytes, checksum: 0a9ae6643b57f22cb912b16233de25aa (MD5)
Previous issue date: 2008-04-04 |
en |
| dc.format.extent |
20 p. |
en_US |
| dc.relation |
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory |
en_US |
| dc.relation |
|
en_US |
| dc.subject |
Learning Theory |
en_US |
| dc.subject |
Hierarchical Architecture Theory |
en_US |
| dc.subject |
Unsupervised Learning |
en_US |
| dc.subject |
Theory of the Visual Cortex |
en_US |
| dc.title |
On a model of visual cortex: learning invariance and selectivity |
en_US |
