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Nonstationary topological learning with bridges and convex polytopes: the G-EXIN neural network

机译：具有桥和凸多边形的非平稳拓扑学习：G-EXIN神经网络

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Non-stationary topological representation can be addressed in two ways, according to the application: life-long modeling or by forgetting the past. Life-long learning requires neural networks equipped with a tool for judging if a neuron has to be created for tracking the input distribution. It is always implemented as an isotropic criterion (a hypersphere centered at the winner weight vector represents the domain of the neuron). Instead, the G-EXIN neural network, presented here, uses an anisotropic convex polytope, which, models the shape of the neuron neighborhood. This idea allows to consider the boundaries of the Voronoi sets of data and controls the extent of the extrapolation. It also employs a novel kind of edge, called bridge, which carries information on the extent of the distribution time change. Indeed, the analysis of bridges, mainly their density, yields a deeper insight to the kind of non-stationarity. Both artificial and real examples are given of the advantages of this approach with regard to the ESOINN neural network, which is the best existing approach to life-long modeling.
机译：根据应用程序：终身建模或忘记过去，可以以两种方式解决非静止拓扑表示。终身学习需要配备有用于判断的工具的神经网络如果必须创建神经元以跟踪输入分布。它总是被实现为各向同性标准（在胜利者重量载体上以赢子重量载体为中心的低度，表示神经元的域）。相反，这里呈现的G-EXIN神经网络使用各向异性凸多托，其模拟神经元邻域的形状。此想法允许考虑Voronoi数据集的边界，并控制推断的范围。它还采用了一种新颖的边缘，称为桥梁，其携带关于分配时间变化的程度的信息。实际上，桥梁的分析，主要是它们的密度，对这种非公平性的类型深入了解。人为和实际例子都透露了这种方法关于esoinn神经网络的优点，这是终身建模的最佳现有方法。

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