Boosting the Differences: A fast Bayesian classifier neural network

05/31/2000
by   Ninan Sajeeth Philip, et al.
0

A Bayesian classifier that up-weights the differences in the attribute values is discussed. Using four popular datasets from the UCI repository, some interesting features of the network are illustrated. The network is suitable for classification problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/20/2019

Mexican Hat Wavelet Kernel ELM for Multiclass Classification

Kernel extreme learning machine (KELM) is a novel feedforward neural net...
research
01/21/2021

Superiorities of Deep Extreme Learning Machines against Convolutional Neural Networks

Deep Learning (DL) is a machine learning procedure for artificial intell...
research
02/21/2018

Adversarial classification: An adversarial risk analysis approach

Classification problems in security settings are usually contemplated as...
research
11/29/2021

On the rate of convergence of a classifier based on a Transformer encoder

Pattern recognition based on a high-dimensional predictor is considered....
research
05/29/2018

On Robust Trimming of Bayesian Network Classifiers

This paper considers the problem of removing costly features from a Baye...
research
05/31/2000

Distorted English Alphabet Identification : An application of Difference Boosting Algorithm

The difference-boosting algorithm is used on letters dataset from the UC...
research
09/28/2021

Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional data

We introduce a novel type of computationally efficient artificial neural...

Please sign up or login with your details

Forgot password? Click here to reset