Learning Theory
Self-determined Learning Theory
This volume explains that self-determined learning theory is based on the claim that learning is adjustment. This is done by showing that a causal connection exists between opportunities to learn, engagement in those opportunities, adjustments to them, and learning.
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Algorithmic Learning Theory
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(eBooks)
This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4, 2007, colocated with the 10th International Conference on Discovery Science, DS 2007.
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Induction, Algorithmic Learning Theory, and Philosophy
This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference.
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Passion to Learn
This inquiry into autodidactism provided fresh insight into the motivation to learn.
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Statistical Mechanics of Learning
Artificial neural networks provide a simple framework for describing learning from examples.
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Advances in Learning Theory
In recent years, considerable progress has been made in the understanding of problems of learning and generalization. In this context, Intelligence basically means the ability to perform well on new data after learning a model on the basis of given data.
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