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
The nature of statistical learning theory
The nature of statistical learning theory
Support vector density estimation
Advances in kernel methods
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
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One major problem in pattern recognition is estimating probability density functions. Unfortunately, parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown density function. On the other hand, non parametric techniques, such as the popular kn-Nearest Neighbor (not to be confused with the k-Nearest Neighbor classification algorithm), allow to remove such an assumption. Albeit effective, the kn-Nearest Neighbor is affected by a number of limitations. Artificial neural networks are, in principle, an alternative family of nonparametric models. So far, artificial neural networks have been extensively used to estimate probabilities (e.g., class-posterior probabilities). However, they have not been exploited to estimate instead probability density functions. This paper introduces a simple, neural-based algorithm for unsupervised, non parametric estimation of multivariate densities, relying on the kn-Nearest Neighbor technique. This approach overcomes the limitations of kn Nearest Neighbor, possibly improving the estimation accuracy of the resulting pdf models. An experimental investigation of the algorithm behavior is offered, exploiting random sampIes drawn from a mixture of Fisher-Tippett density functions.