A computationally and cognitively plausible model of supervised and unsupervised learning
DOI: 10.1007/978-3-642-38786-9_17
关键词: AdaBoost 、 Association (psychology) 、 Computer science 、 Perceptron 、 Artificial intelligence 、 Unsupervised learning 、 Semi-supervised learning 、 Machine learning
摘要: Both empirical and mathematical demonstrations of the importance chance-corrected measures are discussed, a new model learning is proposed based on psychological results association learning. Two forms this developed, Informatron as Perceptron, AdaBook AdaBoost procedure. Computational presented show chance correction facilitates
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