NDLI: Classification boundaries and gradients of trained multilayer perceptrons

Content Provider	IEEE Xplore Digital Library
Author	Hwang, J.-N. Choi, J.J. Oh, S. Marks II, R.J.
Copyright Year	1990
Description	Author affiliation: Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA (Hwang, J.-N.; Choi, J.J.; Oh, S.; Marks, R.J., II)
Abstract	An approach for query-based neural network learning is presented. Consider a layered perceptron partially trained for binary classification. The single output neuron is trained to be either a 0 or a 1. A test decision is made by thresholding the output at, for instance, 1/2. The set of inputs that produce an output of 1/2 forms the classification boundary. An inversion algorithm is adopted for the neural network that allows generation of this boundary. In addition, the classification gradient can be generated for each boundary point. The gradient provides a useful measure of the sharpness of the multidimensional decision surfaces. Using the boundary point and gradient information, conjugate input pair locations are generated and presented to an oracle for proper classification. These data are used to further refine the classification boundary, thereby increasing the classification accuracy. The result can be a significant reduction in the training set cardinality in comparison with, for example, randomly generated data points.<>
Starting Page	3256
Ending Page	3259
File Size	295004
Page Count	4
File Format	PDF
DOI	10.1109/ISCAS.1990.112706
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-05-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multilayer perceptrons Neural networks Neurons Iterative algorithms Testing Nonhomogeneous media Feedforward systems Multi-layer neural network Feedforward neural networks Nonlinear equations
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

GMDP: a novel unified neuron model for multilayer feedforward neural networks

Prediction of nonlinear dynamical system output with multilayer perceptron and radial basis function neural networks

A new feedforward neural network hidden layer neuron pruning algorithm

Dynamically deactivating hidden neurons in a multilayer perceptron neural network

An efficient multilayer quadratic perceptron for pattern classification and function approximation

Bounds on number of hidden neurons of multilayer perceptrons in classification and recognition

A unifying viewpoint of multilayer perceptrons and hidden Markov models

Using multithreshold quadratic sigmoidal neurons to improve classification capability of multilayer perceptrons

Analysis of gradient descent learning algorithms for multilayer feedforward neural networks

Classification boundaries and gradients of trained multilayer perceptrons

Similar Documents

GMDP: a novel unified neuron model for multilayer feedforward neural networks

Prediction of nonlinear dynamical system output with multilayer perceptron and radial basis function neural networks

A new feedforward neural network hidden layer neuron pruning algorithm

Dynamically deactivating hidden neurons in a multilayer perceptron neural network

An efficient multilayer quadratic perceptron for pattern classification and function approximation

Bounds on number of hidden neurons of multilayer perceptrons in classification and recognition

A unifying viewpoint of multilayer perceptrons and hidden Markov models

Using multithreshold quadratic sigmoidal neurons to improve classification capability of multilayer perceptrons

Analysis of gradient descent learning algorithms for multilayer feedforward neural networks

Classification boundaries and gradients of trained multilayer perceptrons