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inria-00105183, version 1

From Informational Confidence to Informational Intelligence

Stefan Jaeger () 1

Tenth International Workshop on Frontiers in Handwriting Recognition (2006)

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.

  • 1:  Laboratory for Language and Media Processing (LAMP)
  • University of Maryland at College Park
  • Domain : Computer Science/Document and Text Processing
    Computer Science/Computer Vision and Pattern Recognition
  • Keywords : Classifier Combination – Sensor Fusion – Information Theory – Machine Learning – Japanese Character Recognition
  • Comment : http://www.suvisoft.com
 
  • inria-00105183, version 1
  • oai:hal.inria.fr:inria-00105183
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  • Submitted on: Tuesday, 10 October 2006 15:13:25
  • Updated on: Tuesday, 10 October 2006 16:58:18