## Adaptive Processing of Sequences and Data Structures: International Summer School on Neural Networks, "E.R. Caianiello", Vietri Sul Mare, Salerno, Italy, September 6-13, 1997, Tutorial LecturesThis book is devoted to adaptive processing of structured information similar to flexible and intelligent information processing by humans - in contrast to merely sequential processing of predominantly symbolic information within a deterministic framework. Adaptive information processing allows for a mixture of sequential and parallel processing of symbolic as well as subsymbolic information within deterministic and probabilistic frameworks. The book originates from a summer school held in September 1997 and thus is ideally suited for advanced courses on adaptive information processing and advanced learning techniques or for self-instruction. Research and design professionals active in the area of neural information processing will find it a valuable state-of-the-art survey. |

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里边有关于DBN的东西，应该写的不错！

### Contents

An Overview | 1 |

Gradient Based Learning Methods | 27 |

Diagrammatic Methods for Deriving and Relating Temporal Neural Network Algorithms | 63 |

An Introduction to Learning Structured Information | 99 |

Neural Networks for Processing Data Structures | 121 |

Topics in Complexity | 145 |

Learning Dynamic Bayesian Networks | 168 |

Probabilistic Models of Neuronal Spike Trains | 198 |

Temporal Models in Blind Source Separation | 229 |

Recursive Neural Networks and Automata | 248 |

Architecture Dynamics and Training | 296 |

Neural Dynamics with Stochasticity | 346 |

The Value of EventBased Segmentation in a Complex RealWorld Control Problem | 370 |

Overview and New Research Directions | 389 |

Predictive Models for Sequence Modelling Application to Speech and Character Recognition | 418 |

### Other editions - View all

Adaptive Processing of Sequences and Data Structures: International Summer ... C.Lee Giles,Marco Gori No preview available - 2003 |

Adaptive Processing of Sequences and Data Structures C. Lee Giles,Marco Gori No preview available - 2014 |

### Common terms and phrases

adjoint network approach approximation assume automata automaton backpropagation Bayesian network binary complexity consider corresponding data structures defined denote density derivatives deterministic discrete DOAG dynamic encoding equations error function estimate event-based segmentation example extraction FCHLRNN feedback feedforward filter finite finite state automata finite-state Gaussian given gradient descent grammar grammatical inference hidden layer neurons hidden Markov models IEEE implemented independent input string input symbol labeled learning algorithms length likelihood linear Markov models matrix methods minimal multilayer perceptrons network architecture Neural Computation neurons NNPDA node nonlinear optimal P/poly parameters perceptron posterior probability probabilistic probabilistic Turing machine probability quantization recurrent networks recurrent neural networks representation represented RNN architecture sequence shown signal space speech recognition spike stack reading statistical independence step stochastic networks supersource synapses temporal Theorem tion training set transduction transposed update values variables vector weights