Optimisation Strategies for Nonconvex Functions and Applications to Neural Networks

摘要：

In this paper the authors describe some useful strategies for nonconvex optimisation in order to determine the global minimum of the error function of a Multi-Layer Perceptron. The proposed approach is founded on a new concept, called non-suspiciousness , which can be seen as a generalisation of convexity.Relations both with classical unconstrained optimisation results and with recent contributions in the field of supervised neural networks are examined. The preliminary numerical experiences show that the. ideas behind the illustrated algorithm are interesting, although they require further investigations.

作者: Carmine Di Fiore Stefano Fanelli Paolo Zellini

作者单位: Dipartimento di Matematica, Uaiversita di Roma Tor VergataVia della Ricerca Scientifica, 00133 Roma, Italy

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 491-496

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）