Bayes Error Estimation Using Parzen and k-NN Procedures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Aided and Automatic Target Recognition Based Upon Sensory Inputs From Image Forming Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mutual Information Theory for Adaptive Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector Quantization Technique for Nonparametric Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Parzen Density Estimation Using Clustering-Based Branch and Bound
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying Mutual Information to Adaptive Mixture Models
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
An Empirical Study of Self/Non-self Discrimination in Binary Data with a Kernel Estimator
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
IEEE Transactions on Image Processing
Bayesian texture classification and retrieval based on multiscale feature vector
Pattern Recognition Letters
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The Parzen density estimate is known to be an effective tool for estimating the Bayes error, given a set of training samples from the class distributions. An algorithm is developed to select a given number of representative samples whose Parzen density estimate closely matches that of the entire sample set. Using this reduced representative set, a piecewise quadratic classifier which provides nearly optimal performance is designed.