Search Results for author: Paul M. Baggenstoss

Found 7 papers, 0 papers with code

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

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.

General Classification

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.

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).

Trainable Compound Activation Functions for Machine Learning

no code implementations25 Apr 2022 Paul M. Baggenstoss

Activation functions (AF) are necessary components of neural networks that allow approximation of functions, but AFs in current use are usually simple monotonically increasing functions.

BIG-bench Machine Learning Dimensionality Reduction

A Comparison of PDF Projection with Normalizing Flows and SurVAE

no code implementations24 Nov 2023 Paul M. Baggenstoss, Felix Govaers

Normalizing flows (NF) recently gained attention as a way to construct generative networks with exact likelihood calculation out of composable layers.

Projected Belief Networks With Discriminative Alignment for Acoustic Event Classification: Rivaling State of the Art CNNs

no code implementations20 Jan 2024 Paul M. Baggenstoss, Kevin Wilkinghoff, Felix Govaers, Frank Kurth

The PBN is two networks in one, a FFNN that operates in the forward direction, and a generative network that operates in the backward direction.

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