Tag RCE

Polars streaming avoids out-of-memory errors in large cross joins via processing data in chunks. Learn how to run 27M row workloads on a single machine.

Refactoring an RCE machine learning algorithm from Pandas lambda functions to the Polars expression API reduced execution time from six minutes to fourteen seconds. Polars cross joins, columnar operations, and Apache Arrow drive a 25x speedup.

Refactor a Reduced Coulomb Energy neural network implementation from Matlab into R Tidyverse with pipes, tibbles, functional operations, and vectorized distance calculations. Compares loop-based Matlab patterns with tidy data workflows for...

In part one of this two-part series, I developed a Reduced Columb Energy (RCE) classifier in Python. RCE calculates hit footprints around training data and uses the footprints to classify test data. RCE draws a circle around each labeled training...

In Pattern Classification Using Neural Networks (IEEE Communications Magazine, Nov. 1989) Richard P. Lippman provides the following definition of Exemplar neural net classifiers: [Exemplar classifiers] perform classification based on the identity...

Introduction Machine Learning engineers use Probabilistic Neural Networks (PNN) for classification and pattern recognition tasks. PNN use a Parzen Window along with a non-negative kernel function to estimate the probability distribution function...

Introduction I investigate the effectiveness of a Reduced Coulomb Energy (RCE) Neural Network on the classification of the University of California, Irvine (UCI) Bupa liver disorder data set. I investigate seven (7) different versions of the data...