Hierarchical Learning Machines and Neuroscience of Visual Cortex

author: Tomaso A. Poggio, McGovern Institute for Brain Research, Massachusetts Institute of Technology, MIT
published: Nov. 16, 2010,   recorded: September 2010,   views: 366
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Description

Learning is the gateway to understanding intelligence and to reproducing it in machines. A classical example of learning algorithms is provided by regularization in Reproducing Kernel Hilbert Spaces. The corresponding architecture however is different from the deep hierarchies found in the brain. I will sketch a new attempt (with S. Smale) to develop a mathematics for hierarchical kernel machines – centered around the notion of a recursively defined “derived kernel” – and directly suggested by the neuroscience of the visual cortex.

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