By Sebastian Thrun

Lifelong studying addresses events within which a learner faces a chain of other studying initiatives offering the chance for synergy between them. Explanation-based neural community studying (EBNN) is a desktop studying set of rules that transfers wisdom throughout a number of studying projects. while confronted with a brand new studying activity, EBNN exploits area wisdom amassed in past studying projects to lead generalization within the new one. consequently, EBNN generalizes extra correctly from much less info than related equipment. Explanation-Based Neural community studying: A Lifelong LearningApproach describes the elemental EBNN paradigm and investigates it within the context of supervised studying, reinforcement studying, robotics, and chess.
`The paradigm of lifelong studying - utilizing prior realized wisdom to enhance next studying - is a promising path for a brand new iteration of desktop studying algorithms. Given the necessity for extra actual studying equipment, it really is tough to visualize a destiny for laptop studying that doesn't comprise this paradigm.'
From the Foreword by means of Tom M. Mitchell.

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Additional resources for Explanation-Based Neural Network Learning: A Lifelong Learning Approach

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Unlike in our previous testbeds, the domain knowledge is not learned through previous game-playing experiences. Instead, NeuroChess uses human expert knowledge represented by a large database of grand-master chess games to learn its domain theory. Empirical results elucidate the role of domain knowledge in chess. They also illustrate, to some extent, the limitations of learning methods that seek to learn to play chess by exclusively considering the final outcome of games. Chapter 6 concludes the book.

2. Secondly, they shall show that EBNN works well in tasks whose complexity is beyond that of artificial, noise-free toy tasks. 3. Thirdly, they shall demonstrate that EBNN compares favorably to other, comparable approaches, particularly those that are unable to transfer knowledge. We conjecture that the lifelong learning framework, and with it EBNN, is relevant for many practical applications of machine learning. Many learning tasks studied in the context of robotics, pattern recognition, time series prediction, information retrieval, database mining and various other tasks can indeed be casted as lifelong learning problems.

Thus, these constraints guide the base-level learning of new functions in a knowledgeable, domain-specific way. EBNN integrates two fundamental learning paradigms that have been studied intensely in the field of machine learning: the analytical and the inductive paradigm. In a nutshell, analytical learning generalizes training instances based upon a theory of the domain. The theory is employed to identify the relevance of the individual training example features. If the domain theory is correct and complete, analytical learning mechanisms typically exhibit superior generalization capabilities.

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