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Displaying similar documents to “A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components”

Kernel Ho-Kashyap classifier with generalization control

Jacek Łęski (2004)

International Journal of Applied Mathematics and Computer Science

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This paper introduces a new classifier design method based on a kernel extension of the classical Ho-Kashyap procedure. The proposed method uses an approximation of the absolute error rather than the squared error to design a classifier, which leads to robustness against outliers and a better approximation of the misclassification error. Additionally, easy control of the generalization ability is obtained using the structural risk minimization induction principle from statistical learning...

Graphics processing units in acceleration of bandwidth selection for kernel density estimation

Witold Andrzejewski, Artur Gramacki, Jarosław Gramacki (2013)

International Journal of Applied Mathematics and Computer Science

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The Probability Density Function (PDF) is a key concept in statistics. Constructing the most adequate PDF from the observed data is still an important and interesting scientific problem, especially for large datasets. PDFs are often estimated using nonparametric data-driven methods. One of the most popular nonparametric method is the Kernel Density Estimator (KDE). However, a very serious drawback of using KDEs is the large number of calculations required to compute them, especially...

Analysis of correlation based dimension reduction methods

Yong Joon Shin, Cheong Hee Park (2011)

International Journal of Applied Mathematics and Computer Science

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Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised...

Profile analysis of mothers susceptible to contaminant exposure in the Algarve region: Application of the HJ-BIPLOT method

A. Serafim, R. Company, B. Lopes, N. Silva, E. Castela, M.J. Bebianno, G. Castela (2012)

Biometrical Letters

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The HJ-BIPLOT method developed by Galindo (1986) was applied in order to identify and categorize mothers vulnerable to environmental contamination in the Algarve region (South Portugal). The application of the BIPLOT method made it possible to recognize the most important exposure routes for contamination, showing that workplace, diet and smoking habits seem the most significant factors contributing to maternal and foetal exposure vulnerability