Artificial Neural Networks and Their Application to Sequence Recognition

Connectionist models; Learning theory; The back-propagation algorithm; Introduction to back-propagation; Formal description; Heuristics to improve convergence and generalization; Extensions; Integrating domain knowledge and learning from examples; Automatic speech recognition; Importance of pre-processing input data; Input coding. Input invariances; Importance of architecture constraints on the network; Modularization; Output coding; Sequence analysis; Introduction; Time delay neural networks; Recurrent networks; BPS; Supervision of a recurrent network does not need to be everywhere; Problems with training of recurrent networks; Dynamic programming post-processors; Hidden Markov models; Integrating ANNs with other systems; Advantages and disadvantages of current algorithms for ANNs; Modularization and joint optimization; Radial basis functions and local representation; Radial basis funtions networks; Neurobiological plausibility; Relation to vector quantization, clustering and semi-continuous HMMs; Methodology; Experiments on phoneme recognition with RBFs; Density estimation with a neural network; Relation between input PDF and output PDF; Density estimation; Conclusion; Post-processors based on dynamic programming; ANN/DP hybrids; ANN/HMM Hybrids; ANN/HMM Hybrid: Phoneme recognition experiments; ANN/HMM hybrid: online handwriting recognition experiments.