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JMLR
2006

A Hierarchy of Support Vector Machines for Pattern Detection

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A Hierarchy of Support Vector Machines for Pattern Detection
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. Initially, SVMs are constructed for each cell with no constraints. This "free network" is then perturbed, cell by cell, into another network, which is "graded" in two ways: first, the number of support vectors of each SVM is reduced (by clustering) in order to adjust to a pre-determined, increasing function of cell depth; second, the decision boundaries are shifted to preserve all positive responses from the original set of training data. The limits on the numbers of clusters (virtual suppo...
Hichem Sahbi, Donald Geman
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JMLR
Authors Hichem Sahbi, Donald Geman
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