Paper Info
Title
The Size of a Hyperball in a Conceptual Space.
Abstract
The cognitive framework of conceptual spaces [3] provides geometric means for representing knowledge. A conceptual space is a high-dimensional space whose dimensions are partitioned into so-called domains. Within each domain, the Euclidean metric is used to compute distances. Distances in the overall space are computed by applying the Manhattan metric to the intra-domain distances. Instances are represented as points in this space and concepts are represented by regions. In this paper, we derive a formula for the size of a hyperball under the combined metric of a conceptual space. One can think of such a hyperball as the set of all points having a certain minimal similarity to the hyperball's center.
Year
Venue
Field
2017
arXiv: Artificial Intelligence
Data mining,Computer science,Euclidean distance,Conceptual space,Theoretical computer science
DocType
Volume
Citations 
Journal
abs/1708.05263
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Lucas Bechberger132.76