Specht D F
Lockheed Missiles and Space Co. Inc., Palo Alto, CA.
IEEE Trans Neural Netw. 1990;1(1):111-21. doi: 10.1109/72.80210.
Two methods for classification based on the Bayes strategy and nonparametric estimators for probability density functions are reviewed. The two methods are named the probabilistic neural network (PNN) and the polynomial Adaline. Both methods involve one-pass learning algorithms that can be implemented directly in parallel neural network architectures. The performances of the two methods are compared with multipass backpropagation networks, and relative advantages and disadvantages are discussed. PNN and the polynomial Adaline are complementary techniques because they implement the same decision boundaries but have different advantages for applications. PNN is easy to use and is extremely fast for moderate-sized databases. For very large databases and for mature applications in which classification speed is more important than training speed, the polynomial equivalent can be found.
回顾了基于贝叶斯策略的两种分类方法以及概率密度函数的非参数估计器。这两种方法分别称为概率神经网络（PNN）和多项式Adaline。两种方法都涉及单遍学习算法，可直接在并行神经网络架构中实现。将这两种方法的性能与多遍反向传播网络进行了比较，并讨论了相对优缺点。PNN和多项式Adaline是互补技术，因为它们实现相同的决策边界，但在应用中有不同优势。PNN易于使用，对于中等规模的数据库速度极快。对于非常大的数据库以及分类速度比训练速度更重要的成熟应用，可以找到多项式等效方法。
