A Method for Handwritten Digits Classification
| Autor: | Zhao JiuLing, Zhao JiuFen, Yan Su, Li JunYing, Ma HuDong |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Contextual image classification
Artificial neural network Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Machine learning computer.software_genre Kernel principal component analysis Statistical classification Kernel (linear algebra) ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Principal component analysis Radial basis function kernel Unsupervised learning Artificial intelligence Tree kernel business computer |
| Zdroj: | ICNC (2) |
| DOI: | 10.1109/icnc.2008.389 |
| Popis: | Kernel PCA, as an unsupervised learning method, is a nonlinear extension of PCA for finding projections that give useful nonlinear descriptors of the data. In the application of handwritten digits classification, kernel based algorithms are indeed highly competitive on a variety of problems with different characteristics. In most real-world pattern analysis tasks, kernel-based can cut the correlative features and prefer discriminable, reliable, independent and optimal features to reduce the complexity of the classifier. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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