IEEE Trans Neural Netw Learn Syst. 2012 Mar;23(3):385-98. doi: 10.1109/TNNLS.2011.2181866.
Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning..
竞争学习是一种重要的机器学习方法，广泛应用于人工神经网络。在本文中，我们提出了一种在大规模网络上实现的新型竞争学习方案的严格定义。该模型由几个粒子在网络中行走，并相互竞争以占据尽可能多的节点，同时试图拒绝入侵粒子。粒子的行走规则由随机和优先运动的随机组合组成。该模型已应用于解决社区检测和数据聚类问题。计算机模拟表明，所提出的技术具有较高的社区和聚类检测精度，以及较低的计算复杂度。此外，我们还开发了一种有效的方法来估计最可能的聚类数量，使用评估器指数来监测竞争过程本身生成的信息。我们希望本文将为竞争学习的研究提供一种替代方法。
