About: M-theory (learning framework)

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In machine learning and computer vision, M-theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-theory was later applied to other areas, such as speech recognition. On certain image recognition tasks, algorithms based on a specific instantiation of M-theory, HMAX, achieved human-level performance.

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rdfs:label	M-theory (learning framework) (en)
rdfs:comment	In machine learning and computer vision, M-theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-theory was later applied to other areas, such as speech recognition. On certain image recognition tasks, algorithms based on a specific instantiation of M-theory, HMAX, achieved human-level performance. (en)
dcterms:subject	Machine learning Computer vision Speech recognition
Wikipage page ID	44632031 (xsd:integer)
Wikipage revision ID	1097282782 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Compact group Compressed sensing Computer vision Machine learning Two-streams hypothesis Feature space Johnson–Lindenstrauss lemma Locally compact group Computer vision Facial recognition system Visual cortex Speech recognition Group (mathematics) Speech recognition Support (mathematics) Dot product Unitary group Neocognitron Sample complexity Gabor function
sameAs	M-theory (learning framework) M-theory (learning framework)
dbp:wikiPageUsesTemplate	dbt:About dbt:Clarify dbt:Orphan dbt:Reflist
has abstract	In machine learning and computer vision, M-theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-theory was later applied to other areas, such as speech recognition. On certain image recognition tasks, algorithms based on a specific instantiation of M-theory, HMAX, achieved human-level performance. The core principle of M-theory is extracting representations invariant under various transformations of images (translation, scale, 2D and 3D rotation and others). In contrast with other approaches using invariant representations, in M-theory they are not hardcoded into the algorithms, but learned. M-theory also shares some principles with compressed sensing. The theory proposes multilayered hierarchical learning architecture, similar to that of visual cortex. (en)
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page length (characters) of wiki page	26288 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:M-theory_(learning_framework)
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is Wikipage redirect of	M-Theory (learning framework)
is foaf:primaryTopic of	wikipedia-en:M-theory_(learning_framework)

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