Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminative Common Vector Method With Kernels
IEEE Transactions on Neural Networks
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This present work presents different feature reduction methods, applied to Deoxyribonucleic Acid (DNA) marker, and in order to identify a success of 100% based on Discriminate Common Vectors (DCV), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) using as classifiers Support Vector Machines (SVM) and Artificial Neural Networks. In particular, the biochemical parameterization has 89 Random Amplified polymorphic DNA (RADPS) markers of Pejibaye palm landraces, and it has been reduced from 89 to a 3 characteristics, for the best method using ICA. The interest of this application is due to feature reduction and therefore, the reduction of computational load time versus the use of all features. This method allows having a faster supervised classification system for the process of the plant certification with origin denomination. Therefore, this system can be transferred to voice applications in order to reduce load time, keeping or improving the success rates.