IEEE

Learning classifiers without negative examples: A reduction approach

Abstract: The problem of PU Learning, i.e., learning classifiers with positive and unlabelled examples (but not negative examples), is very important in information retrieval and data mining. We ...
Microsoft

Denoised smoothing: Provably defending pretrained classifiers against adversarial examples

Editor’s note: This post and its research are the result of the collaborative efforts of a team of researchers comprising former Microsoft Research Engineer Hadi Salman (opens in new tab), CMU PhD ...
Frontiers

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for ...

1 Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, United States 2 Department of Biomedical and Pharmaceutical Sciences, ...
GitHub

evaluateClassifiers_examples.rst

In the PyRBP, we integrate several machine learning classifiers from sklearn and implement several classical deep learning models for users to perform performance tests, for which we provide two ...
IEEE

Indoor Localization Using Visible Light Via Fusion of Multiple Classifiers

Abstract: We propose a localization technique by fusing multiple classifiers based on received signal strengths (RSSs) of visible light in which different intensity-modulated sinusoidal signals ...
POWER Magazine

Dynamic classifiers improve pulverizer performance and more

No one in this industry underestimates the difficulty of transforming an unwieldy and distinctly nonuniform substance like coal into a fuel whose physical and chemical characteristics are consistent ...