J
John Platt
Researcher at Microsoft
Publications - 369
Citations - 66980
John Platt is an academic researcher from Microsoft. The author has contributed to research in topics: Support vector machine & Artificial neural network. The author has an hindex of 83, co-authored 369 publications receiving 60242 citations. Previous affiliations of John Platt include Google & California Institute of Technology.
Papers
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Patent
Audio processing system and method for classifying speakers in audio data
Chris J.C. Burges,John Platt +1 more
TL;DR: In this article, an audio processing system and method for classifying speakers in audio data using a discriminatively-trained classifier is presented, where the anchor model outputs are mapped to frame tags to that all speech corresponding to a single frame tag comes from a single speaker.
Patent
Constructing a table of music similarity vectors from a music similarity graph
TL;DR: The Music Mapper as mentioned in this paper automatically constructs a set coordinate vectors for use in inferring similarity between various pieces of music, which is then used in constructing music playlists given one or more random or user selected seed songs or in a statistical music clustering process.
Proceedings Article
Leaning by Combining Memorization and Gradient Descent
TL;DR: A radial basis function network that allocates a new computational unit whenever an unusual pattern is presented to the network, which learns much faster than do those using back-propagation and uses a comparable number of synapses.
Journal ArticleDOI
Achievement of Sustained Net Plasma Heating in a Fusion Experiment with the Optometrist Algorithm
E. A. Baltz,Erik Trask,Michl Binderbauer,M. Dikovsky,Hiroshi Gota,R. Mendoza,John Platt,Patrick Riley +7 more
TL;DR: This innovative technique led to the discovery of an unexpected record confinement regime with positive net heating power in a field-reversed configuration plasma, characterised by a >50% reduction in the energy loss rate and concomitant increase in ion temperature and total plasma energy.
Proceedings Article
Finding similar failures using callstack similarity
Kevin Bartz,Jack W. Stokes,John Platt,Ryan S. Kivett,David Grant,Silviu C. Calinoiu,Gretchen Loihle +6 more
TL;DR: A machine-learned similarity metric for Windows failure reports using telemetry data gathered from clients describing the failures is developed and results of a failure similarity classifier based on this and other features are presented.