Probabilistic neural-network structure determination for pattern classification

By K. Mao, K. Tan, Wee Ser. IEEE Trans. Neural Networks Learn. Syst., 2000

297 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/803c78c30b24b39bf9d4f5916c358f2cea36e846

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jooho Choi
2. Dawn An
3. Nam-Ho Kim

Citations in the corpus

• Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, background): “However, most papers employ the PNN for classification or risk diagnosis [75,76], and the PNN for prognosis is rarely found, except for the study by Khawaja et al.”
• EEG feature extraction based on wavelet packet decomposition for brain computer interface (2008, methodology): “By virtue of easy training and a solid statistical foundation in Bayesian estimation theory, the PNN has become an effective tool for solving many classification problems [27] The neural network structure can be illustrated in Fig.”
• Enhanced probabilistic neural network with local decision circles: A robust classifier (2010, unknown): “This is the only parameter used to optimize the performance of PNN [55,87].”