C
Charles W. Anderson
Researcher at Colorado State University
Publications - 136
Citations - 8865
Charles W. Anderson is an academic researcher from Colorado State University. The author has contributed to research in topics: Artificial neural network & Reinforcement learning. The author has an hindex of 35, co-authored 129 publications receiving 8182 citations. Previous affiliations of Charles W. Anderson include University of Manitoba & University of Massachusetts Amherst.
Papers
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Proceedings ArticleDOI
NADALINE connectionist learning vs. linear regression at a lamp manufacturing plant
TL;DR: The results show that a simple connectionist algorithm can operate using limited computing power, online, and give a meaningful interpretation of a manufacturing process.
Book ChapterDOI
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
TL;DR: In this paper, the authors introduce temporal neighborhoods as small groups of states that experience frequent intra-group transitions during on-line sampling, and form basis functions along these temporal neighborhoods.
Journal ArticleDOI
Workshops of the seventh international brain-computer interface meeting: not getting lost in translation
Jane E. Huggins,Christoph Guger,Erik J. Aarnoutse,Brendan Z. Allison,Charles W. Anderson,Steven Bedrick,Walter G. Besio,Ricardo Chavarriaga,Jennifer L. Collinger,An H. Do,Christian Herff,Matthias R. Hohmann,Michelle Kinsella,Kyuhwa Lee,Fabien Lotte,Gernot Müller-Putz,Anton Nijholt,Elmar Pels,Betts Peters,Felix Putze,Rüdiger Rupp,Gerwin Schalk,Stephanie M. Scott,Michael Tangermann,Paul Tubig,Thorsten O. Zander +25 more
TL;DR: This paper summarizes each workshop at the BCI Meeting, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.
Proceedings ArticleDOI
EEG subspace analysis and classification using principal angles for Brain-Computer Interfaces
Rehab Ashari,Charles W. Anderson +1 more
TL;DR: It is shown that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application.
Journal ArticleDOI
Word Clustering as a Feature for Arabic Sentiment Classification
TL;DR: This article demonstrates the ongoing work that utilizes word clustering when conducting Arabic sentiment analysis by combining the clustering feature with sentiment analysis for Arabic and improved the performance of the classifier.