Abstract: Most employed Bayesian algorithms, such as quadratic discriminant analysis, linear discriminant analysis or naive Bayes, rely on Gaussian assumptions. In this letter we introduce a novel ...
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ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...
Currently the examples in the documentation are limited to regression. Adding a simple example for classification on a relatively small dataset (two moons, MNIST, CIFAR10 etc.) would help illustrate ...
Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the naive Bayes regression technique, where the goal is to predict a single numeric value. Compared to other ...
The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...
Comparing composite models for multi-component observational data is a prevalent scientific challenge. When fitting composite models, there exists the potential for systematics from a poor fit of one ...
ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
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