Accelerated learning in layered neural networks
Complex Systems
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural Networks
Links between Markov models and multilayer perceptrons
Advances in neural information processing systems 1
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of Digital Communication and Coding
Principles of Digital Communication and Coding
Minimum Cross-Entropy Approximation for Modeling of Highly Intertwining Data Sets at Subclass Levels
Journal of Intelligent Information Systems
Audio-Visual Speaker Recognition for Video Broadcast News
Journal of VLSI Signal Processing Systems
Combined Classification of Handwritten Digits Using the 'Virtual Test Sample Method'
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
Holistic cursive word recognition based on perceptual features
Pattern Recognition Letters
Probabilistic Neural Network Based Method for Fault Diagnosis of Analog Circuits
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Regularized margin-based conditional log-likelihood loss for prototype learning
Pattern Recognition
| Hi-index | 0.14 |
A probabilistic interpretation is presented for two important issues in neural network based classification, namely the interpretation of discriminative training criteria and the neural network outputs as well as the interpretation of the structure of the neural network. The problem of finding a suitable structure of the neural network can be linked to a number of well established techniques in statistical pattern recognition, such as the method of potential functions, kernel densities, and continuous mixture densities. Discriminative training of neural network outputs amounts to approximating the class or posterior probabilities of the classical statistical approach. This paper extends these links by introducing and analyzing novel criteria such as maximizing the class probability and minimizing the smoothed error rate. These criteria are defined in the framework of class-conditional probability density functions. We will show that these criteria can be interpreted in terms of weighted maximum likelihood estimation, where the weights depend in a complicated nonlinear fashion on the model parameters to be trained. In particular, this approach covers widely used techniques such as corrective training, learning vector quantization, and linear discriminant analysis.