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
Universal approximation using radial-basis-function networks
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Conditional Probability Estimation: Forecasting beyond Point Predictions
Neural Networks for Conditional Probability Estimation: Forecasting beyond Point Predictions
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Networks with trainable amplitude of activation functions
Neural Networks
Comparison of combined probabilistic connectionist models in a forensic application
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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Estimation of probability density functions (pdf) is one major topic in pattern recognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. Artificial neural networks (ANN) are, in principle, an alternative family of nonparametric models. ANNs are intensively used to estimate probabilities (e.g., class-posterior probabilities), but they have not been exploited so far to estimate pdfs. This paper introduces a simple neural-based algorithm for unsupervised, nonparametric estimation of pdfs, relying on PW. The approach overcomes the limitations of PW, possibly leading to improved pdf models. An experimental demonstration of the behavior of the algorithm w.r.t. PW is presented, using random samples drawn from a standard exponential pdf.