Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.Wi...Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional展开更多
A reinforcement clustering framework which constitutes Bernoulli stochastic neural units is proposed inthis paper. Reinforcement learning mechanism is introduced to LVQ clustering problems. Related algorithm LVQ-Ris d...A reinforcement clustering framework which constitutes Bernoulli stochastic neural units is proposed inthis paper. Reinforcement learning mechanism is introduced to LVQ clustering problems. Related algorithm LVQ-Ris developed and its property is analyzed in detail. The authors conclude that reinforcement learning can be also intro-duced to other on-line competitive clustering methods. Experiments show that LVQ-R has better performance than o-riginal LVQ approach.展开更多
文摘Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional
文摘A reinforcement clustering framework which constitutes Bernoulli stochastic neural units is proposed inthis paper. Reinforcement learning mechanism is introduced to LVQ clustering problems. Related algorithm LVQ-Ris developed and its property is analyzed in detail. The authors conclude that reinforcement learning can be also intro-duced to other on-line competitive clustering methods. Experiments show that LVQ-R has better performance than o-riginal LVQ approach.
