L
Lutz Hamel
Researcher at University of Rhode Island
Publications - 46
Citations - 631
Lutz Hamel is an academic researcher from University of Rhode Island. The author has contributed to research in topics: Phylogenetic tree & Knowledge extraction. The author has an hindex of 9, co-authored 44 publications receiving 566 citations. Previous affiliations of Lutz Hamel include University of Oxford & University of New Hampshire.
Papers
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Book
Knowledge Discovery with Support Vector Machines
TL;DR: Knowledge Discovery with Support Vector Machines (KVM) as mentioned in this paper provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material.
Journal ArticleDOI
Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (Ixodes scapularis)
Kathryn Berger,Kathryn Berger,Howard S. Ginsberg,Howard S. Ginsberg,Katherine D Dugas,Lutz Hamel,Thomas N. Mather +6 more
TL;DR: A method to forecast LB risk in endemic regions is described and the predictive role of microclimatic moisture conditions on tick encounter risk is identified, suggesting the possibility to more accurately predict tick abundance and human LB incidence.
Book ChapterDOI
Model Assessment with ROC Curves
TL;DR: A look at model performance metrics derived from the confusion matrix are highlighted and how ROC curves can be deployed for model assessment in order to provide a much deeper and perhaps more intuitive analysis of the models.
Proceedings ArticleDOI
Visualization of Support Vector Machines with Unsupervised Learning
TL;DR: A novel visualization technique of support vector machines based on unsupervised learning, specifically self-organizing maps that allows for the visualization of high-dimensional datasets together with their support vector models.
Journal ArticleDOI
Self-Organizing Map Convergence
Robert Tatoian,Lutz Hamel +1 more
TL;DR: A new quality measure called the convergence index, a linear combination of map embedding accuracy and estimated topographic accuracy, is introduced that captures the notion that a SOM has learned the multivariate distribution of a training data set by looking at the convergence of the marginals.