Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Representation and Reality
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Karma: knowledge-based active representations for metaphor and aspect
Karma: knowledge-based active representations for metaphor and aspect
When push comes to shove: a computational model of the role of motor control in the acquisition of action verbs
Learning words from sights and sounds: a computational model
Learning words from sights and sounds: a computational model
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Music artist style identification by semi-supervised learning from both lyrics and content
Proceedings of the 12th annual ACM international conference on Multimedia
Audio data model for multi-criteria query formulation and retrieval
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
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The audio bitstream in music encodes a high amount of statistical, acoustic, emotional and cultural information. But music also has an important linguistic accessory; most musical artists are described in great detail in record reviews, fan sites and news items. We highlight current and ongoing research into extracting relevant features from audio and simultaneously learning language features linked to the music. We show results in a "query-by-description" task in which we learn the perceptual meaning of automatically-discovered single-term descriptive components, as well as a method of automatically uncovering 'semantically attached' terms (terms that have perceptual grounding.) We then show recent work in 'semantic basis functions' --- parameter spaces of description (such as fast ... slow or male ... female) that encode the highest descriptive variance in a semantic space.