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Learning to Compare using Operator-Valued Large-Margin Classifiers
Published on Feb 25, 20073482 Views
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 elementa
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Chapter list
a binary classification task for pairs00:00
a binary classification task for pairs0101:18
a binary classification task for pairs0202:28
a binary classification task for pairs0302:45
pair classifiers induced by linear transformations03:15
pair classifiers induced by linear transformations0104:49
pair classifiers induced by linear transformations0205:42
estimation and generalization07:45
estimation and generalization0109:27
regularized objectives11:45
regularized objectives0112:53
optimization problem14:25
optimization problem0114:41
optimization problem0215:05
algorithm15:30
experiments15:46
rotation- and scale-invariant character recognition16:33
rotation- and scale-invariant character recognition0116:56
rotation- and scale-invariant character recognition0217:05
results for rotation/scale-invariant OCR17:13