T. K. Lau, "Rival Penalized Competitive Learning for Content-based Indexing", Master's thesis, The Chinese University of Hong Kong, Hong Kong, 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....model to be meaningful to explain the index efficiency of the RPCL b tree. It is very helpful to analyze the index efficiency of the RPCL b tree for different data distributions, and compare the index efficiency between RPCL b tree and the other indexing structures. 1. 2 Previous Work In [2, 3, 4, 5], we analyzed the influence of boundary problem for different data distributions, and then proposed the non hierarchical and hierarchical indexing structure based on the RPCL clustering which is an unsupervised neural network learning algorithm. Using RPCL can quickly and accurately estimate the ....

....of the nearest neighbor search. To ensure the index accuracy is 100 , we can implement backtracking based on a branch and bound algorithm [1] In this branch and bound algorithm, each node is tested to determine whether or not possibly containing the nearest neighbor to a query by a set of rules [5]. 3 Regression Model for RPCL b tree 3.1 Index Efficiency Measurement In this paper, we define the index efficiency measurement, Y ,as Y =1, # of distance computations for the checked method # of distance computations in linear search : 1) With the measurement, the efficiency of the linear ....

T.K. Lau. Rival penalized competitive learning for content-based indexing. Master's thesis, The Chinese University of Hong Kong, 1998.

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T. K. Lau, "Rival Penalized Competitive Learning for Content-based Indexing", Master's thesis, The Chinese University of Hong Kong, Hong Kong, 1998.

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