✦ LIBER ✦

Constructive training of probabilistic neural networks

✍ Scribed by Michael R. Berthold; Jay Diamond

Book ID: 114296851
Publisher: Elsevier Science
Year: 1998
Tongue: English
Weight: 292 KB
Volume: 19
Category: Article
ISSN: 0925-2312
DOI: 10.1016/s0925-2312(97)00063-5

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Novelty detection with constructive prob

Novelty detection with constructive probabilistic neural networks

✍ Adriano L.I. Oliveira; Flavio R.G. Costa; Clovis O.S. Filho 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 602 KB

Constructive training of recurrent neura

Constructive training of recurrent neural networks using hybrid optimization

✍ Niranjan Subrahmanya; Yung C. Shin 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 1010 KB

Robust maximum likelihood training of he

Robust maximum likelihood training of heteroscedastic probabilistic neural networks

✍ Zheng Rong Yang; Sheng Chen 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 246 KB

We consider the probabilistic neural network (PNN) that is a mixture of Gaussian basis functions having different variances. Such a Gaussian heteroscedastic PNN is more economic, in terms of the number of kernel functions required, than the Gaussian mixture PNN of a common variance. The expectation-

Probabilistic neural networks

✍ Donald F. Specht 📂 Article 📅 1990 🏛 Elsevier Science 🌐 English ⚖ 863 KB

By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network (PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also d

Probabilistic reasoning and probabilisti

Probabilistic reasoning and probabilistic neural networks

✍ Gerhard Pass 📂 Article 📅 1992 🏛 John Wiley and Sons 🌐 English ⚖ 642 KB

The Boltzmann machine is a probabilistic neural network describing the associative dependency of variables. It yields a probability distribution, which is a special case of the distribution generated by probabilistic inference networks. Hence both types of networks can be combined allowing to integr

Implementing probabilistic Neural Networ

Implementing probabilistic Neural Networks

✍ Fabio Ancona; Anna Maria Colla; Stefano Rovetta; Rodolfo Zunino 📂 Article 📅 1997 🏛 Springer-Verlag 🌐 English ⚖ 808 KB