A computationally and cognitively plausible model of supervised and unsupervised learning
This work addresses the need for more plausible learning models in machine learning and psychology, but it appears incremental as it builds on existing methods like Perceptron and AdaBoost.
The paper tackled the problem of improving learning models by incorporating chance-corrected measures, proposing new models like the Informatron and AdaBook, and showed that chance correction facilitates learning with computational results.
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.