Kohonen Learning with a mechanism, the Law of the Jungle, Capable of Dealing with Nonstationary Probability Distribution Functions

摘要

We present a mechanism, named the law of the jungle (LOJ), to improve the Kohonen learning. The LOJ is used to be an adaptive vector quantizer for approximating nonstation- ary probability distribution functions. In the LOJ mechanism, the probability that each node wins in a competition is dynami- cally estimated during the learning. By using the estimated win probability, "strong" nodes are increased through creating new nodes near the nodes, and "weak" nodes are decreased through deleting themselves. A pair of creation and deletion is treated as an atomic operation.