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Neural networks / Data analysis / Statistical classification / Support vector machine / Supervised learning / K-nearest neighbor algorithm / Dimension reduction / Pattern recognition / Degrees of freedom / Statistics / Machine learning / Multivariate statistics


Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure Ruslan Salakhutdinov and Geoffrey Hinton Department of Computer Science University of Toronto
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Document Date: 2007-03-17 14:55:15

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File Size: 2,64 MB

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City

San Mateo / Cambridge / /

Company

Neural Information Processing Systems / MIT Press / Hertz / AAAI Press / /

Currency

USD / /

Facility

Computer Science University of Toronto Toronto / /

IndustryTerm

Information processing / encoder network / energy term / deep nonlinear encoder network / computing / feature extraction algorithm / dynamical systems / layer networks / learning algorithm / energy / /

Organization

MIT / Geoffrey Hinton Department / Computer Science University of Toronto Toronto / /

Person

Daphna Weinshall / Ruslan Salakhutdinov / Stuart J. Russell / Andrew Y. Ng / Noam Shental / S. T. Roweis / Sam Roweis / Michael I. Jordan / Aharon Bar-Hillel / A. Globerson / J. Goldberger / Geoffrey Hinton / G. E. Hinton / Eric P. Xing / Tomer Hertz / Morgan Kaufmann / /

Position

CRC chair / /

ProgrammingLanguage

J / DC / K / /

ProvinceOrState

California / Massachusetts / Ontario / /

PublishedMedium

IEEE Transactions on Information Theory / Machine Learning / Journal of Machine Learning Research / /

Technology

neural network / unsupervised algorithm / nonlinear NCA algorithm / machine learning / feature extraction algorithm / /

URL

http /

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