Academic Journal
Classification of reduction invariants with improved backpropagation
| Title: | Classification of reduction invariants with improved backpropagation |
|---|---|
| Authors: | Siti Mariyam Shamsuddin, Maslina Darus, Md. Nasir Sulaiman |
| Source: | International Journal of Mathematics and Mathematical Sciences, Vol 30, Iss 4, Pp 239-247 (2002) |
| Publisher Information: | Wiley, 2002. |
| Publication Year: | 2002 |
| Subject Terms: | Artificial neural network, Artificial intelligence, Principal component analysis, Backpropagation, 02 engineering and technology, Handwriting Recognition and Text Detection, Pattern recognition (psychology), Fabric Defect Detection in Industrial Applications, Industrial and Manufacturing Engineering, Engineering, Shape Matching and Object Recognition, QA1-939, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Feature Descriptors, Data mining, Pattern recognition, speech recognition, Pure mathematics, Document Image Analysis, Fabric Defect Detection, Dimensionality reduction, Computer science, Dimension (graph theory), Computer Science, Physical Sciences, Wafer Map Defect Classification, data reduction, Feature extraction, Surface Defect Detection, Computer Vision and Pattern Recognition, Mathematics |
| Description: | Data reduction is a process of feature extraction that transforms the data space into a feature space of much lower dimension compared to the original data space, yet it retains most of the intrinsic information content of the data. This can be done by using a number of methods, such as principal component analysis (PCA), factor analysis, and feature clustering. Principal components are extracted from a collection of multivariate cases as a way of accounting for as much of the variation in that collection as possible by means of as few variables as possible. On the other hand, backpropagation network has been used extensively in classification problems such as XOR problems, share prices prediction, and pattern recognition. This paper proposes an improved error signal of backpropagation network for classification of the reduction invariants using principal component analysis, for extracting the bulk of the useful information present in moment invariants of handwritten digits, leaving the redundant information behind. Higher order centralised scale‐ invariants are used to extract features of handwritten digits before PCA, and the reduction invariants are sent to the improved backpropagation model for classification purposes. |
| Document Type: | Article Other literature type |
| File Description: | application/xml |
| Language: | English |
| ISSN: | 1687-0425 0161-1712 |
| DOI: | 10.1155/s0161171202006117 |
| DOI: | 10.60692/byckc-7nm91 |
| DOI: | 10.60692/2gazm-w8m06 |
| Access URL: | http://downloads.hindawi.com/journals/ijmms/2002/390584.pdf https://doaj.org/article/b49a096f059441f38d85ea1763b3d263 https://doaj.org/article/b49a096f059441f38d85ea1763b3d263 https://downloads.hindawi.com/journals/ijmms/2002/390584.pdf https://www.emis.de/journals/HOA/IJMMS/Volume30_4/390584.pdf https://www.maths.tcd.ie/EMIS/journals/HOA/IJMMS/30/4239.pdf http://emis.maths.adelaide.edu.au/journals/HOA/IJMMS/30/4239.pdf http://gmm.fsksm.utm.my/~mariyam/PUBLISHED_PAPERS_DRCTM/YEAR_2000-2002/IJMMS_2000.pdf |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....8dbd50fc4dda52b94ed51a35a04da20f |
| Database: | OpenAIRE |
| ISSN: | 16870425 01611712 |
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| DOI: | 10.1155/s0161171202006117 |