Explanation-Based Neural Network Learning A Lifelong Learning Approach
Başlık:
Explanation-Based Neural Network Learning A Lifelong Learning Approach
ISBN:
9781461313816
Personal Author:
Edition:
1st ed. 1996.
Yayın Bilgileri:
New York, NY : Springer US : Imprint: Springer, 1996.
Fiziksel Tanımlama:
XVI, 264 p. online resource.
Series:
The Springer International Series in Engineering and Computer Science ; 357
Contents:
1 Introduction -- 1.1 Motivation -- 1.2 Lifelong Learning -- 1.3 A Simple Complexity Consideration -- 1.4 The EBNN Approach to Lifelong Learning -- 1.5 Overview -- 2 Explanation-Based Neural Network Learning -- 2.1 Inductive Neural Network Learning -- 2.2 Analytical Learning -- 2.3 Why Integrate Induction and Analysis? -- 2.4 The EBNN Learning Algorithm -- 2.5 A Simple Example -- 2.6 The Relation of Neural and Symbolic Explanation-Based Learning -- 2.7 Other Approaches that Combine Induction and Analysis -- 2.8 EBNN and Lifelong Learning -- 3 The Invariance Approach -- 3.1 Introduction -- 3.2 Lifelong Supervised Learning -- 3.3 The Invariance Approach -- 3.4 Example: Learning to Recognize Objects -- 3.5 Alternative Methods -- 3.6 Remarks -- 4 Reinforcement Learning -- 4.1 Learning Control -- 4.2 Lifelong Control Learning -- 4.3 Q-Learning -- 4.4 Generalizing Function Approximators and Q-Learning -- 4.5 Remarks -- 5 Empirical Results -- 5.1 Learning Robot Control -- 5.2 Navigation -- 5.3 Simulation -- 5.4 Approaching and Grasping a Cup -- 5.5 NeuroChess -- 5.6 Remarks -- 6 Discussion -- 6.1 Summary -- 6.2 Open Problems -- 6.3 Related Work -- 6.4 Concluding Remarks -- A An Algorithm for Approximating Values and Slopes with Artificial Neural Networks -- A.1 Definitions -- A.2 Network Forward Propagation -- A.3 Forward Propagation of Auxiliary Gradients -- A.4 Error Functions -- A.5 Minimizing the Value Error -- A.6 Minimizing the Slope Error -- A.7 The Squashing Function and its Derivatives -- A.8 Updating the Network Weights and Biases -- B Proofs of the Theorems -- C Example Chess Games -- C.1 Game 1 -- C.2 Game 2 -- References -- List of Symbols.
Abstract:
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.
Ek Kurum Yazarı:
Elektronik Erişim:
Full Text Available From Springer Nature Engineering Archive Packages
Dil:
English