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Modeling Data Streams Using Sparse Distributed Representations

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Published on May 23, 2012

In this screencast, Jeff Hawkins narrates the presentation he gave at a workshop called "From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications." The workshop was held May 7-11, 2012 at the University of California, Berkeley.

Slides: http://www.numenta.com/htm-overview/0...

Abstract:
Sparse distributed representations appear to be the means by which brains encode information. They have several advantageous properties including the ability to encode semantic meaning. We have created a distributed memory system for learning sequences of sparse distribute representations. In addition we have created a means of encoding structured and unstructured data into sparse distributed representations. The resulting memory system learns in an on-line fashion making it suitable for high velocity data streams. We are currently applying it to commercially valuable data streams for prediction, classification, and anomaly detection In this talk I will describe this distributed memory system and illustrate how it can be used to build models and make predictions from data streams.

Live video recording of this presentation: http://www.youtube.com/watch?v=nfUT3U...

General information can be found at https://www.numenta.com, and technical details can be found in the CLA white paper at https://www.numenta.com/faq.html#cla_....

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