The EURASIP workshop contributions collected in this volume have an interdisciplinary character. The authors include psychologists, biologists, engineers and mathematicians as well as computer scientists. The volume starts with two invited papers, by George Cybenko and by Eric Baum, on the formal study of the capabilities of neural networks. The following papers are organized into parts dealing with theory and algorithms, speech processing, image processing, and implementation. The workshop was sponsored by the European Association for Signal Processing without restriction on the origin of participants.|This proceedings volume starts with two invited papers on the formal study of the capabilities of neural networks. The following papers are organized into parts dealing with theory and algorithms, speech processing, image processing, and implementation.
When are k-nearest neighbor and back propagation accurate for feasible sized sets of examples?.- Complexity theory of neural networks and classification problems.- Generalization performance of overtrained back-propagation networks.- Stability of the random neural network model.- Temporal pattern recognition using EBPS.- Markovian spatial properties of a random field describing a stochastic neural network: Sequential or parallel implementation?.- Chaos in neural networks.- The "moving targets” training algorithm.- Acceleration techniques for the backpropagation algorithm.- Rule-injection hints as a means of improving network performance and learning time.- Inversion in time.- Cellular neural networks: Dynamic properties and adaptive learning algorithm.- Improved simulated annealing, Boltzmann machine, and attributed graph matching.- Artificial dendritic learning.- A neural net model of human short-term memory development.- Large vocabulary speech recognition using neural-fuzzy and concept networks.- Speech feature extraction using neural networks.- Neural network based continuous speech recognition by combining self organizing feature maps and Hidden Markov Modeling.- Ultra-small implementation of a neural halftoning technique.- Application of self-organising networks to signal processing.- A study of neural network applications to signal processing.- Simulation machine and integrated implementation of neural networks.- VLSI implementation of an associative memory based on distributed storage of information.
When are k-nearest neighbor and back propagation accurate for feasible sized sets of examples?.- Complexity theory of neural networks and classification problems.- Generalization performance of overtrained back-propagation networks.- Stability of the random neural network model.- Temporal pattern recognition using EBPS.- Markovian spatial properties of a random field describing a stochastic neural network: Sequential or parallel implementation?.- Chaos in neural networks.- The "moving targets" training algorithm.- Acceleration techniques for the backpropagation algorithm.- Rule-injection hints as a means of improving network performance and learning time.- Inversion in time.- Cellular neural networks: Dynamic properties and adaptive learning algorithm.- Improved simulated annealing, Boltzmann machine, and attributed graph matching.- Artificial dendritic learning.- A neural net model of human short-term memory development.- Large vocabulary speech recognition using neural-fuzzy and concept networks.- Speech feature extraction using neural networks.- Neural network based continuous speech recognition by combining self organizing feature maps and Hidden Markov Modeling.- Ultra-small implementation of a neural halftoning technique.- Application of self-organising networks to signal processing.- A study of neural network applications to signal processing.- Simulation machine and integrated implementation of neural networks.- VLSI implementation of an associative memory based on distributed storage of information.
Inhaltsverzeichnis
When are k-nearest neighbor and back propagation accurate for feasible sized sets of examples?.- Complexity theory of neural networks and classification problems.- Generalization performance of overtrained back-propagation networks.- Stability of the random neural network model.- Temporal pattern recognition using EBPS.- Markovian spatial properties of a random field describing a stochastic neural network: Sequential or parallel implementation?.- Chaos in neural networks.- The ¿moving targets¿ training algorithm.- Acceleration techniques for the backpropagation algorithm.- Rule-injection hints as a means of improving network performance and learning time.- Inversion in time.- Cellular neural networks: Dynamic properties and adaptive learning algorithm.- Improved simulated annealing, Boltzmann machine, and attributed graph matching.- Artificial dendritic learning.- A neural net model of human short-term memory development.- Large vocabulary speech recognition using neural-fuzzy and concept networks.- Speech feature extraction using neural networks.- Neural network based continuous speech recognition by combining self organizing feature maps and Hidden Markov Modeling.- Ultra-small implementation of a neural halftoning technique.- Application of self-organising networks to signal processing.- A study of neural network applications to signal processing.- Simulation machine and integrated implementation of neural networks.- VLSI implementation of an associative memory based on distributed storage of information.
Klappentext
The EURASIP workshop contributions collected in this volume have an interdisciplinary character. The authors include psychologists, biologists, engineers and mathematicians as well as computer scientists. The volume starts with two invited papers, by George Cybenko and by Eric Baum, on the formal study of the capabilities of neural networks. The following papers are organized into parts dealing with theory and algorithms, speech processing, image processing, and implementation. The workshop was sponsored by the European Association for Signal Processing without restriction on the origin of participants.
This proceedings volume starts with two invited papers on the formal study of the capabilities of neural networks. The following papers are organized into parts dealing with theory and algorithms, speech processing, image processing, and implementation.
