Search Results for author: Paul M. Baggenstoss

Found 4 papers, 0 papers with code

The Projected Belief Network Classfier : both Generative and Discriminative

no code implementations14 Aug 2020 Paul M. Baggenstoss

The projected belief network (PBN) is a layered generative network with tractable likelihood function, and is based on a feed-forward neural network (FF-NN).

A Neural Network Based on First Principles

no code implementations18 Feb 2020 Paul M. Baggenstoss

This posterior has a well-defined mean, the conditional mean estimator, that is calculated using a type of neural network with theoretically-derived activation functions similar to sigmoid, softplus, and relu.

Kernel-based Generative Learning in Distortion Feature Space

no code implementations21 Jun 2016 Bo Tang, Paul M. Baggenstoss, Haibo He

The recognition diversity indicates that a hybrid combination of the proposed generative classifier and the discriminative classifier could further improve the classification performance.

Classification General Classification

EEF: Exponentially Embedded Families with Class-Specific Features for Classification

no code implementations11 May 2016 Bo Tang, Steven Kay, Haibo He, Paul M. Baggenstoss

In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features.

Classification Feature Selection +2

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