E
Ernest Fokoué
Researcher at Rochester Institute of Technology
Publications - 94
Citations - 807
Ernest Fokoué is an academic researcher from Rochester Institute of Technology. The author has contributed to research in topics: Support vector machine & Bayesian probability. The author has an hindex of 11, co-authored 87 publications receiving 675 citations. Previous affiliations of Ernest Fokoué include Ohio State University & Kettering University.
Papers
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Book
Principles and Theory for Data Mining and Machine Learning
TL;DR: This book is a thorough introduction to the most important topics in data mining and machine learning that begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods.
Journal ArticleDOI
Mixtures of factor analysers. Bayesian estimation and inference by stochastic simulation
Ernest Fokoué,D. M. Titterington +1 more
TL;DR: A Bayesian approach toFactor Analysis is adopted, and more specifically a treatment that bases estimation and inference on the stochastic simulation of the posterior distributions of interest, and can be envisaged as an alternative to the other approaches used for this model.
Proceedings Article
Efficient Approaches to Gaussian Process Classification
TL;DR: Three simple approximations for the calculation of the posterior mean in Gaussian Process classification are presented, based on Bayesian online approach which was motivated by recent results in the Statistical Mechanics of Neural Networks.
Posted Content
Robust Classification of High Dimension Low Sample Size Data
Necla Gunduz,Ernest Fokoué +1 more
TL;DR: This work reveals that Random Forest, although not inherently designed to be robust to outliers, substantially outperforms the existing techniques specifically designed to achieve robustness.
Proceedings ArticleDOI
Android Malware Detection Using Category-Based Machine Learning Classifiers
TL;DR: In this article, the authors proposed category-based machine learning classifiers to enhance the performance of classification models at detecting malicious apps under a certain category, and the intensive machine learning experiments proved that categorybased classifiers report a remarkable higher average performance compared to non-category based.