Graph Matching for Shape Retrieval
Abstract
We propose a new in-sample cross validation based method (randomized GACV) for choosing smoothing or bandwidth parameters that govern the bias-variance or fit-complexity tradeoff in 'soft' classification. Soft clas(cid:173) sification refers to a learning procedure which estimates the probability that an example with a given attribute vector is in class 1 vs class O. The target for optimizing the the tradeoff is the Kullback-Liebler distance between the estimated probability distribution and the 'true' probabil(cid:173) ity distribution, representing knowledge of an infinite population. The method uses a randomized estimate of the trace of a Hessian and mimics cross validation at the cost of a single relearning with perturbed outcome data.
Cite
Text
Huet et al. "Graph Matching for Shape Retrieval." Neural Information Processing Systems, 1998.Markdown
[Huet et al. "Graph Matching for Shape Retrieval." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/huet1998neurips-graph/)BibTeX
@inproceedings{huet1998neurips-graph,
title = {{Graph Matching for Shape Retrieval}},
author = {Huet, Benoit and Cross, Andrew D. J. and Hancock, Edwin R.},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {896-902},
url = {https://mlanthology.org/neurips/1998/huet1998neurips-graph/}
}