Rival penalized competitive learning
نویسندگان
چکیده
منابع مشابه
Convergence Analysis of Rival Penalized Competitive Learning (RPCL) Algorithm
This paper analyzes the convergence of the Rival Penalized Competitive Learning (RPCL) algorithm via a cost function. It is shown that as RPCL process decreases the cost to a global minimum, a correct number of weight vectors will converge to each center of the clusters in the sample data, respectively, while the others diverge.
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In this paper, we propose a model-based, competitive learning procedure for the clustering of variable-length sequences. Hidden Markov models (HMMs) are used as representations for the cluster centers, and rival penalized competitive learning (RPCL), originally developed for domains with static, fixed-dimensional features, is extended. State merging operations are also incorporated to favor the...
متن کاملRegression 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 c...
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This paper 1 presents an approach named Rival Penalized Competitive Learning based Binary Source Separator (RPCL-BSS), which has two major advantages: (1) fast in implementation , (2) able to automatically determine the number of binary sources, and (3) able to reduce the noise eeects. Experiments have shown that RPCL-BSS algorithm can not only nd out the correct number of sources quickly, but ...
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The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the c...
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ژورنال
عنوان ژورنال: Scholarpedia
سال: 2007
ISSN: 1941-6016
DOI: 10.4249/scholarpedia.1810