NDLI: The use of recurrent neural networks for classification

Content Provider	IEEE Xplore Digital Library
Author	Burrows, T.L. Niranjan, M.
Copyright Year	1994
Description	Author affiliation: Dept. of Eng., Cambridge Univ., UK (Burrows, T.L.; Niranjan, M.)
Abstract	Recurrent neural networks are widely used for context dependent pattern classification tasks such as speech recognition. The feedback in these networks is generally claimed to contribute to integrating the context of the input feature vector to be classified. This paper analyses the use of recurrent neural networks for such applications. We show that the contribution of the feedback connections is primarily a smoothing mechanism and that this is achieved by moving the class boundary of an equivalent feedforward network classifier. We also show that when the sigmoidal hidden nodes of the network operate close to saturation, switching from one class to the next is delayed, and within a class the network decisions are insensitive to the order of presentation of the input vectors.
Starting Page	117
Ending Page	125
File Size	451083
Page Count	9
File Format	PDF
ISBN	0780320263
DOI	10.1109/NNSP.1994.366057
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-09-06
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Speech recognition Neurofeedback Smoothing methods Delay Feedforward systems Databases Feedback Neural networks Pattern classification
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

Learning the time-delay characteristics in a neural network

A limited feedback time-delay neural network

A recurrent time-delay neural network for improved phoneme recognition

Normalizing internal representations for speech classification

Comparing NN paradigms in hybrid NN/HMM speech recognition using tied posteriors

Recurrent neural networks for phoneme recognition

A modular neural network architecture for pattern classification

Recurrent neural networks for speech modeling and speech recognition

Nonlinear channel equalization using new neural network model

The use of recurrent neural networks for classification

Similar Documents

Learning the time-delay characteristics in a neural network

A limited feedback time-delay neural network

A recurrent time-delay neural network for improved phoneme recognition

Normalizing internal representations for speech classification

Comparing NN paradigms in hybrid NN/HMM speech recognition using tied posteriors

Recurrent neural networks for phoneme recognition

A modular neural network architecture for pattern classification

Recurrent neural networks for speech modeling and speech recognition

Nonlinear channel equalization using new neural network model

The use of recurrent neural networks for classification