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Hierarchical Learning Machines and Neuroscience of Visual Cortex
This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Barcelona 2010. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
