Learning to Compare using Operator-Valued Large-Margin Classifiers

author: Andreas Maurer, Stemmer Imaging

Description

The proposed method uses homonymous and heteronymous examplepairs to train a linear preprocessor on a kernel-induced Hilbert space. The algorithm seeks to optimize the expected performance of elementary classifiers to be generated from single future training examples. The method is justified by PAC-style generalization guarantees and the resulting algorithm has been tested on problems of geometrically invariant pattern recognition and face verification.

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*Slides*
0:00	a binary classification task for pairs
1:18	a binary classification task for pairs01
2:28	a binary classification task for pairs02
2:45	a binary classification task for pairs03
3:15	pair classifiers induced by linear transformations
4:49	pair classifiers induced by linear transformations01
5:42	pair classifiers induced by linear transformations02
7:45	estimation and generalization
9:27	estimation and generalization01
11:45	regularized objectives
12:53	regularized objectives01
14:25	optimization problem
14:41	optimization problem01
15:05	optimization problem02
15:30	algorithm
15:46	experiments
16:33	rotation- and scale-invariant character recognition
16:56	rotation- and scale-invariant character recognition01
17:05	rotation- and scale-invariant character recognition02
17:13	results for rotation/scale-invariant OCR

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