✦ LIBER ✦

Adaptive Evolutionary Artificial Neural Networks for Pattern Classification

✍ Scribed by Tatt Hee Oong; Isa, N.A.M.

Book ID: 117927497
Publisher: IEEE
Year: 2011
Tongue: English
Weight: 525 KB
Volume: 22
Category: Article
ISSN: 1045-9227
DOI: 10.1109/tnn.2011.2169426

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Real-time fMRI pattern-classification us

Real-time fMRI pattern-classification using artificial neural networks

✍ M. Weygandt; R. Stark; C. Blecker; B. Walter; D. Vaitl 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 37 KB

Adaptive basis function for artificial n

Adaptive basis function for artificial neural networks

✍ Ninan Sajeeth Philip; K.Babu Joseph 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 358 KB

Galaxy Classification Using Artificial N

Galaxy Classification Using Artificial Neural Networks

✍ S. C. ODEWAHN 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 523 KB

Pattern recognition using artificial neu

Pattern recognition using artificial neural networks

✍ R.S.H. Mah; V. Chakravarthy 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 655 KB

Pattern classification with artificial n

Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data

✍ J.R.M. Smits; L.W. Breedveld; M.W.J. Derksen; G. Kateman; H.W. Balfoort; J. Snoe 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 942 KB

In this paper the applicability of artificial neural networks as pattern classifiers is investigated To study the behaviour of neural networks as pattern classifier for complex data, the identification and counting of phytoplankton, based upon flow cytometer data, is taken as an example For this pro

Unsupervised pattern classification by n

Unsupervised pattern classification by neural networks

✍ D. Hamad; C. Firmin; J.-G. Postaire 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 449 KB

A neural network is applied to the unsupervised pattern classification approach. Given a set of data consisting of unlabeled samples from several classes, the task of unsupervised classification is to label every sample in the same class by the same symbol such that the data set is divided into seve