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From Informational Confidence to Informational Intelligence

Abstract : This paper is a continuation of my previous work on informational confidence. The main idea of this technique is to normalize confidence values from different sources in such a way that they match their informational content determined by their performance in an application domain. This reduces classifier combination to a simple integration of information. The proposed method has shown good results in handwriting recognition and other applications involving classifier combination. In the present paper, I will focus more on the theoretical properties of my approach. I will show that informational confidence has the potential to serve as a theory for learning in general by showing that this approach naturally leads us to the famous Yin/Yang symbol of Chinese philosophy, a classic symbol describing two opposing forces. Furthermore, a closer inspection of the opposing forces and their interplay will reveal a new information-theoretical meaning of the golden ratio, which describes the points where both confidence and counter-confidence merge into one force, with performance matching expectation. Although this is mainly a theoretical paper, I will present some practical results for handwritten Japanese character recognition.
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Contributor : Anne Jaigu <>
Submitted on : Tuesday, October 10, 2006 - 3:13:25 PM
Last modification on : Friday, August 2, 2019 - 2:30:10 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 7:12:35 PM


  • HAL Id : inria-00105183, version 1



Stefan Jaeger. From Informational Confidence to Informational Intelligence. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct 2006, La Baule (France). ⟨inria-00105183⟩



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