By Topic

Progressive Learning Paradigms using the Parallel K-Iterations Fast Learning Artificial Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ho, R.C.K. ; Nanyang Technol. Univ., Singapore ; Tay, A.L.P. ; Xuejie Zhang

This paper illustrates how progressive network learning can be performed using concepts from the K-iterations fast learning artificial neural network (KFLANN). It is common in certain application domains to require knowledge updating of existing neural networks and many existing learning paradigms require extensive re-training. The KFLANN is an efficient clustering algorithm that provides consistent clusters within a short number of epochs and the paper explains how its capabilities in efficient and consistent clustering aid progressive learning. Progressive learning is useful in domains that handle data samples that arrive at periodic stages over stages of time. The bioinformatics industry is one such domain where data often arrives as segmented blocks during the various stages of bio-molecular experimentation. This work provides an option for progressive learning of partial data that subsequently updates as more complete information is made available. The paper discusses how FLANN, a subset of the KFLANN, can be configured as a progressive learning algorithm to merge segmented data. It further compares the results with those obtained from the typical non-segmented processing. In the process of introducing the progressive learning paradigm in using FLANN, we also present a feasible parallelization algorithm known as the parallel-KFLANN (P-KFLANN).

Published in:

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Date of Conference:

12-17 Aug. 2007