9 citations · 19 across the 4 of their papers we have counts for
3 papers · 1 filter
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
NLARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
Makoto Yamada, Avishek Saha, Hua Ouyang +2
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of t…
Protocols for Learning Classifiers on Distributed Data
Hal Daume, Jeff M. Phillips, Avishek Saha +1
We consider the problem of learning classifiers for labeled data that has been distributed across several nodes. Our goal is to find a single classifier, with small approximation e…