论文标题
有监督和无监督学习的计算和认知上合理的模型
A computationally and cognitively plausible model of supervised and unsupervised learning
论文作者
论文摘要
讨论了经验校正措施的重要性的经验和数学证明，并根据有关关联学习的经验心理结果提出了一种新的学习模型。开发了两种形式的该模型，即作为偶然校正的感知器的信息，而Adabook作为偶然性校正的adaboost程序。提出的计算结果表明，机会校正有助于学习。
Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.
