Introduction to Probabilistic Neural Networks
Introduction to Probabilistic Neural Networks
复制标题
概率神经网络简介
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
L. Rutkowski
中科院分区：
文献类型：
--
作者：
L. Rutkowski
概率神经网络（PNN）由斯佩希特[255] - [258]提出，其前身是统计模式分类理论。在20世纪五六十年代，平稳情况下的统计模式分类问题是通过参数方法解决的，利用了现有的统计数学工具（例如[35]，[75]，[89]，[90]，[293]）。假定已知概率密度（除未知参数外），并根据学习序列对参数进行估计。典型的技术包括最大似然法和贝叶斯方法。观察过去二十年文献中的趋势，我们应该说这些方法几乎完全被非参数方法所取代（例如见[67]，[70]，[71]，[79]，[80]，[81]，[104]，[105]，[113]，[114]，[122]，[175]，[191]，[195]，[272]，[296]，[297]）。在非参数方法中，假定概率密度的函数形式是未知的。后者通过非参数估计器进行估计。
Probabilistic neural networks (PNN), introduced by Specht [255] – [258], have their predecessors in the theory of statistical pattern classification. In the fifties and sixties problems of statistical pattern classification in the stationary case were accomplished by means of parametric methods, using the available apparatus of statistical mathematics (e.g. [35], [75], [89], [90], [293]). The knowledge of the probability density to an accuracy of unknown parameters was assumed and the parameters were estimated based on the learning sequence. Typical techniques included maximum likelihood and Bayesian approaches. Having observed tendencies present in literature within the last twenty years we should say that these methods have been almost completely replaced by the non-parametric approach (see e.g. [67], [70], [71], [79], [80], [81], [104], [105], [113], [114], [122], [175], [191], [195], [272], [296], [297]). In the non-parametric approach it is assumed that a functional form of probability ensities is unknown. The latter are estimated by non-parametric estimators.