Explanation-Based Neural Network Learning [electronic resource] : A Lifelong Learning Approach /

Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.

Bibliographic Details

Main Authors:	Thrun, Sebastian. author., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	Boston, MA : Springer US, 1996
Subjects:	Computer science., Artificial intelligence., Statistical physics., Dynamical systems., Computer Science., Artificial Intelligence (incl. Robotics)., Statistical Physics, Dynamical Systems and Complexity.,
Online Access:	http://dx.doi.org/10.1007/978-1-4613-1381-6
Tags:	Add Tag No Tags, Be the first to tag this record!