Search Results for author: Dan P. Guralnik

Found 6 papers, 0 papers with code

Iterated Belief Revision Under Resource Constraints: Logic as Geometry

no code implementations20 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.

Functorial Hierarchical Clustering with Overlaps

no code implementations8 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.

Clustering

Consistency constraints for overlapping data clustering

no code implementations15 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.

Clustering

Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering

no code implementations25 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.

Clustering Small Data Image Classification

Statistical Properties of the Single Linkage Hierarchical Clustering Estimator

no code implementations24 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.

Clustering

Universal Memory Architectures for Autonomous Machines

no code implementations21 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.

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