Mathematics of the Neural Response

Authors:
S. Smale;L. Rosasco;J. Bouvrie;A. Caponnetto;T. Poggio
Affiliations:
Toyota Technological Institute at Chicago and University of California, Berkeley, CA, USA;Università di Genova, CBCL, McGovern Institute, MIT & DISI, Cambridge, MA, USA;Massachusetts Institute of Technology, CBCL, Brain and Cognitive Sciences, Cambridge, MA, USA;City University of Hong Kong, Department of Mathematics, Hong Kong, China;Massachusetts Institute of Technology, CBCL, McGovern Institute, CSAIL, BCS, Cambridge, MA, USA
Venue:
Foundations of Computational Mathematics
Year:
2010

Citing 0
Cited 4

Kernel Analysis of Deep Networks

The Journal of Machine Learning Research
Similarity learning for object recognition based on derived kernel

Neurocomputing
The derived kernel based recognition method of vehicle type

AMT'12 Proceedings of the 8th international conference on Active Media Technology
Hierarchical kernel-based rotation and scale invariant similarity

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a natural image representation, the neural response, motivated by the neuroscience of the visual cortex. The inner product defined by the neural response leads to a similarity measure between functions which we call the derived kernel. Based on a hierarchical architecture, we give a recursive definition of the neural response and associated derived kernel. The derived kernel can be used in a variety of application domains such as classification of images, strings of text and genomics data.