no code implementations • 20 Dec 2018 • Dan P. Guralnik, Daniel E. Koditschek
We propose a variant of iterated belief revision designed for settings with limited computational resources, such as mobile autonomous robots.
no code implementations • 8 Sep 2016 • Jared Culbertson, Dan P. Guralnik, Peter F. Stiller
Those three are the overlapping clustering of Jardine and Sibson, the functorial approach of Carlsson and M\'{e}moli to partition-based clustering, and the Isbell/Dress school's study of injective envelopes.
no code implementations • 15 Aug 2016 • Jared Culbertson, Dan P. Guralnik, Jakob Hansen, Peter F. Stiller
We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli.
no code implementations • 25 Nov 2015 • Dekang Zhu, Dan P. Guralnik, Xuezhi Wang, Xiang Li, Bill Moran
We derive a statistical model for estimation of a dendrogram from single linkage hierarchical clustering (SLHC) that takes account of uncertainty through noise or corruption in the measurements of separation of data.
no code implementations • 24 Nov 2015 • Dekang Zhu, Dan P. Guralnik, Xuezhi Wang, Xiang Li, Bill Moran
Distance-based hierarchical clustering (HC) methods are widely used in unsupervised data analysis but few authors take account of uncertainty in the distance data.
no code implementations • 21 Feb 2015 • Dan P. Guralnik, Daniel E. Koditschek
We propose a self-organizing memory architecture for perceptual experience, capable of supporting autonomous learning and goal-directed problem solving in the absence of any prior information about the agent's environment.