NDLI: Recurrent genetic programming

Content Provider	IEEE Xplore Digital Library
Author	Teredesai, A. Govindaraju, V. Ratzlaff, E. Subrahmonia, J.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci. & Eng., State Univ. of New York, USA (Teredesai, A.; Govindaraju, V.)
Abstract	A typical pattern recognition system consists of two stages: the pre-processing stage to extract features from the data, and the classification stage to assign the feature vector a class label. There are two kinds of feature extraction techniques with respect to the kind of data: the fixed number of features per sample generating a fixed length feature vector, and the fixed number of features per subsample generating a variable length feature vector due to variable number of sub-samples (frames) for each input pattern. The first kind is the most commonly used feature vector for classification methods. The second kind is usually extracted in domains where the input sample is time-variant. Traditionally a separate class of machine learning algorithms consisting of hidden Markov models, recurrent neural networks, etc. have been employed for classification of time variant signals. Evolutionary computation techniques like genetic algorithms and genetic programming have also been used previously to optimize the architecture for HMMs or learning the weights for recurrent-neural networks. We describe a recurrent framework for genetic programming (GP). This framework helps place GP in the class of machine learning algorithms alongside recurrent neural networks and hidden Markov models. We describe the application of recurrent genetic programming for the classification of on-line handwritten numerals obtained from tablet-based input.
File Size	552585
File Format	PDF
ISBN	0780374371
ISSN	1062922X
DOI	10.1109/ICSMC.2002.1173238
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-10-06
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic programming Hidden Markov models Feature extraction Machine learning algorithms Recurrent neural networks Pattern recognition Data mining Evolutionary computation Genetic algorithms Computer architecture
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in