Pattern classifying neural network based on Fisher's linear discriminant function

Lee, Jong Chan; Kim, Yung Hwan; Lee, Won Don; Lee, Sukhoon

doi:10.1109/ijcnn.1992.287098

[Proceedings 1992] IJCNN International Joint Conference on Neural Networks

DOI: 10.1109/ijcnn.1992.287098

|View full text |Cite

Pattern classifying neural network based on Fisher's linear discriminant function

Jong Chan Lee

Yung Hwan Kim

Won Don Lee

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Other2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Synthesizing neural networks by sequential addition of hidden nodes

Tufts

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

View full text Add to dashboard Cite

Abstroct-Inthis paper, we present a fast training algorithm with which one can sequentially determine t h e needed hidden nodes and t h e values of t h e aseociated weights for classification and pattern recognition. This new approach addresses problems in backpropagation and other gradient descent training algorithms. These problems include long training times, and t h e determination of t h e proper number of hidden nodes. We mitigate these difficulties by sequentially extracting important attributes of t h e training d a t a in training each hidden node.T h e proposed algorithm separates t h e network training into t h e training of each layer of t h e network. The input layer is designed t o partition the input data space using linear discriminant functions. The training starts from one hidden node. By applying a linear discriminant algorithm, a separable subset of t h e data is deleted from t h e training set. The remaining data is carried over t o t h e training of t h e next hidden nodes. A hidden node is added t o t h e network when it isneeded because t h e classification performance on t h e training set is not yet good enough. Thus t h e training data set is reduced sequentially while t h e training is in progress. Each node of t h e output layer performs a logic function of t h e binary outputs of t h e hidden nodes. T h e training algorithm for t h e output layer is same as Boolean minimization.

show abstract

Synthesizing neural networks by sequential addition of hidden nodes

Tufts

Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)

View full text Add to dashboard Cite

show abstract

Principal-feature classification

Tufts

Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Pattern classifying neural network based on Fisher's linear discriminant function

Cited by 2 publications

References 5 publications

Synthesizing neural networks by sequential addition of hidden nodes

Synthesizing neural networks by sequential addition of hidden nodes

Principal-feature classification

Contact Info

Product

Resources

About