A
Ann Nowé
Researcher at Vrije Universiteit Brussel
Publications - 463
Citations - 6848
Ann Nowé is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Reinforcement learning & Learning automata. The author has an hindex of 34, co-authored 441 publications receiving 5253 citations. Previous affiliations of Ann Nowé include Katholieke Universiteit Leuven & VU University Amsterdam.
Papers
More filters
Journal ArticleDOI
A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis
Cosmin Lazar,Jonatan Taminau,Stijn Meganck,David Steenhoff,Alain Coletta,Colin Molter,V. de Schaetzen,Robin Duque,Hugues Bersini,Ann Nowé +9 more
TL;DR: This survey focuses on filter feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery, and presents them in a unified framework.
Journal ArticleDOI
Batch effect removal methods for microarray gene expression data integration: a survey
Cosmin Lazar,Stijn Meganck,Jonatan Taminau,David Steenhoff,Alain Coletta,Colin Molter,David Y. Weiss-Solis,Robin Duque,Hugues Bersini,Ann Nowé +9 more
TL;DR: Methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments are reviewed in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process.
Proceedings ArticleDOI
NRGcoin: Virtual currency for trading of renewable energy in smart grids
TL;DR: In this paper, a new decentralized digital currency, called NRGcoin, is introduced for the smart grid, where prosumers trade locally produced renewable energy using NRGcoins, the value of which is determined on an open currency exchange market.
Proceedings Article
Reinforcement learning from demonstration through shaping
TL;DR: This paper investigates the intersection of reinforcement learning and expert demonstrations, leveraging the theoretical guarantees provided by reinforcement learning, and using expert demonstrations to speed up this learning by biasing exploration through a process called reward shaping.