Sciweavers

Share
IJCNN
2000
IEEE

Regression Analysis for Rival Penalized Competitive Learning Binary Tree

11 years 11 months ago
Regression Analysis for Rival Penalized Competitive Learning Binary Tree
The main aim of this paper is to develop a suitable regression analysis model for describing the relationship between the index efficiency and the parameters of the Rival Penalized Competitive Learning Binary Tree (RPCLb-tree). RPCL-b-tree is a hierarchical indexing structure built with a hierarchical RPCL clustering implementation, which transforms the feature space into a sequence of nested clusters. Based on the RPCL-b-tree, the efficient Nearest-Neighbor search for a query can be performed with the branch-and-bound algorithm. The index efficiency of a RPCL-b-tree relates to a set of parameters: leaf node size of the tree, number of retrieved objects per search, feature dimensionality and database size. To formulate this relationship, we develop a nonlinear regression model in this paper. This regression model includes two components. One is used to describe the relationship between index efficiency and the number of retrieved objects per search; another is to describe the rela...
Xuequn Li, Irwin King
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where IJCNN
Authors Xuequn Li, Irwin King
Comments (0)
books