21,133 research outputs found
Resource Constrained Structured Prediction
We study the problem of structured prediction under test-time budget
constraints. We propose a novel approach applicable to a wide range of
structured prediction problems in computer vision and natural language
processing. Our approach seeks to adaptively generate computationally costly
features during test-time in order to reduce the computational cost of
prediction while maintaining prediction performance. We show that training the
adaptive feature generation system can be reduced to a series of structured
learning problems, resulting in efficient training using existing structured
learning algorithms. This framework provides theoretical justification for
several existing heuristic approaches found in literature. We evaluate our
proposed adaptive system on two structured prediction tasks, optical character
recognition (OCR) and dependency parsing and show strong performance in
reduction of the feature costs without degrading accuracy
Locality-Sensitive Hashing with Margin Based Feature Selection
We propose a learning method with feature selection for Locality-Sensitive
Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays.
These bit arrays can be used to perform similarity searches and personal
authentication. The proposed method uses bit arrays longer than those used in
the end for similarity and other searches and by learning selects the bits that
will be used. We demonstrated this method can effectively perform optimization
for cases such as fingerprint images with a large number of labels and
extremely few data that share the same labels, as well as verifying that it is
also effective for natural images, handwritten digits, and speech features.Comment: 9 pages, 6 figures, 3 table
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