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pro vyhledávání: '"Guralnik, Dan P."'
Akademický článek
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Publikováno v:
Electron. J. Combin. 27 Issue 3 (2020), P3.46
We prove that for all $d \geq 1$ a shellable $d$-dimensional simplicial complex with at most $d+3$ vertices is extendably shellable. The proof involves considering the structure of `exposed' edges in chordal graphs as well as a connection to linear q
Externí odkaz:
http://arxiv.org/abs/1908.07155
We propose a variant of iterated belief revision designed for settings with limited computational resources, such as mobile autonomous robots. The proposed memory architecture---called the {\em universal memory architecture} (UMA)---maintains an epis
Externí odkaz:
http://arxiv.org/abs/1812.08313
We prove several results about chordal graphs and weighted chordal graphs by focusing on exposed edges. These are edges that are properly contained in a single maximal complete subgraph. This leads to a characterization of chordal graphs via deletion
Externí odkaz:
http://arxiv.org/abs/1706.04537
Publikováno v:
Discrete Applied Mathematics, Volume 236, 19 February 2018, pp.108--123
This work draws inspiration from three important sources of research on dissimilarity-based clustering and intertwines those three threads into a consistent principled functorial theory of clustering. Those three are the overlapping clustering of Jar
Externí odkaz:
http://arxiv.org/abs/1609.02513
We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli. This avoids issues arising from the chaining required by partition-based methods. Our principal result shows that any clustering functor is natu
Externí odkaz:
http://arxiv.org/abs/1608.04331
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. Our focus is on just the estimati
Externí odkaz:
http://arxiv.org/abs/1511.07944
Distance-based hierarchical clustering (HC) methods are widely used in unsupervised data analysis but few authors take account of uncertainty in the distance data. We incorporate a statistical model of the uncertainty through corruption or noise in t
Externí odkaz:
http://arxiv.org/abs/1511.07715
We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatial
Externí odkaz:
http://arxiv.org/abs/1507.01637
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. The architecture is simple
Externí odkaz:
http://arxiv.org/abs/1502.06132