A Hierarchy of Support Vector Machines for Pattern Detection
Abstract
We introduce a computational design for pattern detection based on a tree-structured network of support vector machines (SVMs). An SVM is associated with each cell in a recursive partitioning of the space of patterns (hypotheses) into increasingly finer subsets. The hierarchy is traversed coarse-to-fine and each chain of positive responses from the root to a leaf constitutes a detection. Our objective is to design and build a network which balances overall error and computation.
Cite
Text
Sahbi and Geman. "A Hierarchy of Support Vector Machines for Pattern Detection." Journal of Machine Learning Research, 2006.Markdown
[Sahbi and Geman. "A Hierarchy of Support Vector Machines for Pattern Detection." Journal of Machine Learning Research, 2006.](https://mlanthology.org/jmlr/2006/sahbi2006jmlr-hierarchy/)BibTeX
@article{sahbi2006jmlr-hierarchy,
title = {{A Hierarchy of Support Vector Machines for Pattern Detection}},
author = {Sahbi, Hichem and Geman, Donald},
journal = {Journal of Machine Learning Research},
year = {2006},
pages = {2087-2123},
volume = {7},
url = {https://mlanthology.org/jmlr/2006/sahbi2006jmlr-hierarchy/}
}