ISBN: 3-540-64341-9
TITLE: Adaptive Processing of Sequences and Data Structures
AUTHOR: Giles, C.Lee; Gori, Marco (Eds.)
TOC:

Architectures and Learning in Recurrent Neural Networks 
Recurrent Neural Network Architectures: An Overview 1 
A.C. Tsoi 
Gradient Based Learning Methods 27 
A.C. Tsoi 
Diagrammatic Methods for Deriving and Relating Temporal Neural 
Network Algorithms 63 
E.A. Wan and F. Beaufays 
Processing of Data Structures 
An Introduction to Learning Structured Information 99 
P. Frasconi 
Neural Networks for Processing Data Structures 121 
A. Sperduti 
The Loading Problem: Topics in Complexity 145 
M. Gori 
Probabilistic Models 
Learning Dynamic Bayesian Networks 168 
Z. Ghahramani 
Probabilistic Models of Neuronal Spike Trains 198 
P. Baldi 
Temporal Models in Blind Source Separation 229 
L.C. Parra 
Analog vs Symbolic Computation 
Recursive Neural Networks and Automata 248 
M. Maggini 
The Neural Network Pushdown Automaton: Architecture, Dynamics 
and Training 296 
G.Z. Sun, C.L. Giles and H.H. Chen 
Neural Dynamics with Stochasticity 346 
H. T. Siegelmann 
Applications 
Parsing the Stream of Time: The Value of Event-Based Segmentation 
in a Complex Real-World Control Problem 370 
M.C.Mozer and D. Miller 
Hybrid HMM/ANN Systems for Speech Recognition: Overview and 
New Research Directions 389 
H. Bourlard and N. Morgan 
Predictive Models for Sequence Modelling, Application to Speech and 
Character Recognition 418 
P. Gallinari 
END
