Ferrari Stefano, Bellocchio Francesco, Piuri Vincenzo, Borghese N Alberto
Department of Information Technology, Università degli Studi di Milano, Crema, Italy.
IEEE Trans Neural Netw. 2010 Feb;21(2):275-85. doi: 10.1109/TNN.2009.2036438. Epub 2009 Dec 11.
In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding units at smaller scales, where the surface details are located, while data points are being collected. Real-time operation is achieved by exploiting the quasi-local nature of the Gaussian units: through the definition of a quad-tree structure to support their receptive field local network reconfiguration can be obtained. The model has been applied to 3-D scanning, where an updated real-time display of the manifold to the operator is fundamental to drive the acquisition procedure itself. Quantitative results are reported, which show that the accuracy achieved is comparable to that of two batch approaches: batch HRBF and support vector machines (SVMs). However, these two approaches are not suitable to real-time online learning. Moreover, proof of convergence is also given.
本文提出了一种新颖的实时在线网络模型。它源自分层径向基函数（HRBF）模型，在收集数据点时，通过在较小尺度（即表面细节所在之处）自动添加单元来实现增长。利用高斯单元的准局部性质实现实时操作：通过定义四叉树结构来支持其感受野，从而可实现局部网络重构。该模型已应用于三维扫描，在三维扫描中，向操作员实时更新流形的显示对于驱动采集过程本身至关重要。报告了定量结果，结果表明所实现的精度与两种批处理方法（批处理HRBF和支持向量机（SVM））相当。然而，这两种方法不适用于实时在线学习。此外，还给出了收敛性证明。
