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Nonparametric Bayesian Classification: Insights and Clustering Techniques

This work explores nonparametric Bayesian classification methods within the Department of Electrical and Computer Engineering. We present simulation results that demonstrate the effectiveness of these techniques. The focus is on the intuition behind their success, highlighting that it's not merely about boundaries or thresholds. Instead, we emphasize the clustering approach that enables each cluster to closely resemble a Gaussian distribution, enhancing the overall classification performance and providing a deeper understanding of the underlying data structure.

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Nonparametric Bayesian Classification: Insights and Clustering Techniques

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  1. Bayesian Nonparametric Classification Department of Electrical and Computer Engineering Simulation results • Intuition why it works so well • Not the boundary or threshold. But clustering so that each cluster looks more like the distribution (Gaussian).

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