Patch Learning for Incremental Classifier Design
R Sicard, T Artières and E Petit
In: ECAI 2006, 28 Aug - 01 Sept, Italy.

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Abstract

We present a learning algorithm for nominal data. It builds a classifier by adding iteratively a simple patch function that modifies the current classifier. Its main advantage lies in the possibility to learn every patch function parameters optimally from the Bayesian point of view hence avoiding overtraining.

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Available Versions of this Item

• Patch Learning for Incremental Classifier Design (deposited 22 November 2006) [Currently Displayed]
  • Learning iteratively a classifier with the Bayesian Model Averaging Principlestar (deposited 14 February 2008)

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