Abstract
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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© 2010 Springer-Verlag Berlin Heidelberg
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Poggio, T. (2010). Hierarchical Learning Machines and Neuroscience of Visual Cortex. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15880-3_5
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DOI: https://doi.org/10.1007/978-3-642-15880-3_5
Publisher Name: Springer, Berlin, Heidelberg
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