Pattern Recognition by Hierarchical Temporal Memory

Maltoni, Prof. Davide (2011) Pattern Recognition by Hierarchical Temporal Memory. [Departmental Technical Report] (Unpublished)

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Hierarchical Temporal Memory (HTM) is still largely unknown by the pattern recognition community and only a few studies have been published in the scientific literature. This paper reviews HTM architecture and related learning algorithms by using formal notation and pseudocode description. Novel approaches are then proposed to encode coincidence-group membership (fuzzy grouping) and to derive temporal groups (maxstab temporal clustering). Systematic experiments on three line-drawing datasets have been carried out to better understand HTM peculiarities and to extensively compare it against other well-know pattern recognition approaches. Our results prove the effectiveness of the new algorithms introduced and that HTM, even if still in its infancy, compares favorably with other existing technologies.

Item Type:Departmental Technical Report
Subjects:Neuroscience > Computational Neuroscience
Computer Science > Machine Vision
Computer Science > Neural Nets
ID Code:9187
Deposited By: Maltoni, Prof. Davide
Deposited On:25 Feb 2014 12:49
Last Modified:25 Feb 2014 12:49


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