The statistical mechanics of learning a rule

Reviews of Modern Physics

Volumn 65, Issue 2, 1993, Pages 499-556

  Watkin, Timothy L H ^a,c   Rau, Albrecht ^a,d   Biehl, Michael ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF WÜRZBURG   (Germany)

^c ST JOHN S COLLEGE   (United Kingdom)

^d Patentanwälte Schwabe Sandmair Marx ^*  (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 21144484106     PISSN: 00346861     EISSN: None     Source Type: Journal    
DOI: 10.1103/RevModPhys.65.499     Document Type: Article

References (213)

1. The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning
2. The AdaTron: An Adaptive Perceptron Algorithm
3. What Size Net Gives Valid Generalization?
4. On-Line Learning of a Time-Dependent Rule
5. Stability properties of Potts neural networks with biased patterns and low loading
6. Exhaustive Thermodynamical Analysis of Boolean Learning Networks
7. Perceptron beyond the limit of capacity
8. An Exactly Solvable Asymmetric Neural Network Model
9. Storing and Retrieving Information in a Layered Spin System
10. The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks
11. Maximum Storage Capacity in Neural Networks
12. Optimal storage properties of neural network models
13. A Growth Algorithm for Neural Network Decision Trees
14. The physics of neural networks
15. Memorization Without Generalization in a Multilayered Neural Network
16. Learning from Examples in a Single-Layer Neural Network
17. Improving a Network Generalization Ability by Selecting Examples
18. Neurocomputing: Algorithms, Architectures and Applications
19. GENERALIZATION ERROR AND DYNAMICAL EFFECTS IN A TWO-DIMENSIONAL PATCHES DETECTOR
20. Storage capacity of memory networks with binary couplings
21. Statistical mechanics for networks of graded-response neurons
22. A Practical Bayesian Framework for Backpropagation Networks
23. A Convergence Theorem for Sequential Learning in Two-Layer Perceptrons
24. The Offset Algorithm: Building and Learning Method for Multilayer Neural Networks
25. Solvable models of working memories
26. On the capacity of feedforward layered networks
27. Learning the Unlearnable
28. STUDY OF A GROWTH ALGORITHM FOR A FEEDFORWARD NETWORK
29. Storage capacity of a Potts-perceptron
30. Learning in Neural Networks: Solvable Dynamics
31. Generalization of Rules by Neural Nets
32. Learning Networks of Neurons with Boolean Logic
33. Experiments on learning by back propagation
34. Generalization in a Large Committee Machine
35. Generalization in a fully-connected committee machine
36. Potts and Related Glasses
37. Learning in a Two-Layer Neural Network of Edge Detectors
38. The Hebb Rule for Learning Linearly Separable Boolean Functions: Learning and Generalization
39. Linear and Nonlinear Extension of the Pseudo-Inverse Solution for Learning Boolean Functions
40. On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
41. Optimal learning with a neural network
42. Learning multi-class classification problems