The Computational Theory of Intelligence: Information Entropy

• 作者: Daniel Kovach
• 来源期刊: 现代非线性理论与应用(英文)
• 学科: 工学
• 关键词: MACHINE LEARNING Artificial INTELLIGENCE ENTROPY COMPUTER SCIENCE INTELLIGENCE
• 年，卷（期）: xdfxxllyyyyw_2014,（4）
• 页码范围: 182-190
• 页数: 9页
• 分类号: TP3

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.