PROBABILISTIC INTENTION CLASSIFICATION FOR HUMAN AUGMENTED COGNITION SYSTEM
Byunghun Hwang, Young-Min Jang, Minho Lee
Abstract:
In this paper, we present a probabilistic human implicit intention classification using user’s eye gaze data for human augmented cognition system. The Ultimate purpose of this method is to implement a human augmented cognition system which can provide a specific service to address the cognitive limitations of human brain. In order to partially overcome the cognitive limitations, the system should be able to control the flow of information. Therefore, a specific intention classification using a Naïve Bayes classifier can be used as useful tool for searching and retrieving specific information according to the human intention and situation.
Keywords:
human intention, Naïve Bayes, human augmented cognition, system architecture
Download:
IMEKO-WC-2012-TC18-P4.pdf
DOI:
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Event details
Event name:
XX IMEKO World Congress
Title:

Metrology for Green Growth

Place:
Busan, REPUBLIC of KOREA
Time:
09 September 2012 - 12 September 2012
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