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16

ICANN
2007
Springer

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Incremental and Decremental Learning for Linear Support Vector Machines

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Incremental and Decremental Learning for Linear Support Vector Machines

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Abstract. We present a method to ﬁnd the exact maximal margin hyperplane for linear Support Vector Machines when a new (existing) component is added (removed) to (from) the inner product. The maximal margin hyperplane with the new inner product is obtained in terms of that for the old inner product, without re-computing it from scratch and the procedure is reversible. An algorithm to implement the proposed method is presented, which avoids matrix inversions from scratch. Among the possible applications, we ﬁnd feature selection and the design of kernels out of similarity measures.

Enrique Romero, Ignacio Barrio, Lluís Belan

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Added	08 Jun 2010
Updated	08 Jun 2010
Type	Conference
Year	2007
Where	ICANN
Authors	Enrique Romero, Ignacio Barrio, Lluís Belanche

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