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Recursive Update Algorithm for Least Squares Support Vector Machines

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Abstract

In this Letter an efficient recursive update algorithm for least squares support vector machines (LSSVMs) is developed. Using the previous solution and some matrix equations, the algorithm completely avoids training the LSSVM all over again whenever new training sample is available. The gain in speed using the recursive update algorithm is illustrated on four data sets from UCI repository: the Statlog Australian credit, the Pima Indians diabetes, the Wisconsin breast cancer, and the adult income data sets.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Purdue University, W. Lafayette, IN, 47907-1285

    Hoi-Ming Chi & Okan K. Ersoy

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  1. Hoi-Ming Chi
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  2. Okan K. Ersoy
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Chi, HM., Ersoy, O.K. Recursive Update Algorithm for Least Squares Support Vector Machines. Neural Processing Letters 17, 165–173 (2003). https://doi.org/10.1023/A:1023634220639

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