@inproceedings{9c898750b6204ae9b30388a8ad6387f7,
title = "A two-level learning hierarchy for constructing incremental projection generalizing neural networks",
abstract = "One of the incremental learning-based neural networks that theoretically guarantees the optimal generalization capability and provides exactly the same generalization capability as that obtained by batch learning is incremental projection generalizing neural networks. This paper will describe a two-level learning hierarchy for constructing the networks. An incremental projection learning in neural networks algorithm is employed at the lower level to construct the network while the learning parameters, the orders of the reproducing kernel Hilbert space, are optimized using a genetic algorithm at the upper level. The networks produced by this learning hierarchy will be used as subsystem of the artificial odor discrimination system to approximate percentage of alcohol.",
keywords = "Artificial neural networks, Genetic algorithms, Hilbert space, Kernel, Mathematics, Neural networks, Neurons, Radio access networks, Resource management, Sampling methods",
author = "Hendri Murfi and Putro, {Benyamin Kusumo}",
note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; Asia-Pacific Conference on Circuits and Systems, APCCAS 2002 ; Conference date: 28-10-2002 Through 31-10-2002",
year = "2002",
doi = "10.1109/APCCAS.2002.1115332",
language = "English",
series = "IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "541--546",
booktitle = "Proceedings - APCCAS 2002",
address = "United States",
}