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The Computational Theory of Intelligence: Information Entropy

The Computational Theory of Intelligence: Information Entropy
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摘要 This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computation alintelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications. This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computation alintelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications.
作者 Daniel Kovach
机构地区 Kovach Technologies
出处 《International Journal of Modern Nonlinear Theory and Application》 2014年第4期182-190,共9页 现代非线性理论与应用(英文)
关键词 MACHINE LEARNING Artificial INTELLIGENCE ENTROPY COMPUTER SCIENCE INTELLIGENCE Machine Learning Artificial Intelligence Entropy Computer Science Intelligence
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