Wyniki wyszukiwania - BazTech

1

Iteratively reweighted least squares classifier and its l2- and l1-regularized Kernel versions

Łęski J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2010

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Vol. 58, nr 1

171-182

EN

This paper introduces a new classifier design method based on regularized iteratively reweighted least squares criterion function. The proposed method uses various approximations of misclassification error, including: linear, sigmoidal, Huber and logarithmic. Using the represented theorem a kernel version of classifier design method is introduced. The conjugate gradient algorithm is used to minimize the proposed criterion function. Furthermore, .1-regularized kernel version of the classifier is introduced. In this case, the gradient projection is used to optimize the criterion function. Finally, an extensive experimental analysis on 14 benchmark datasets is given to demonstrate the validity of the introduced methods.

2

Kernel Ho-Kashyap classifier with generalization control

Łęski J.

International Journal of Applied Mathematics and Computer Science

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2004

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Vol. 14, no 1

53-61

EN

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 theory. Finally, examples are given to demonstrate the validity of the introduced method.

3

Membership function - ARTMAP neural networks

Sinčák P., Hric M., Vaščák J.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2003

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Vol. 7, No 1

43-52

EN

The project deals with the application of computational intelligence (CI) tools for multispectral image classification. Pattern Recognition scheme is a global approach where the classification part is playing an important role to achieve the highest classification accuracy. Multispectral images are data mainly used in remote sensing and this kind of classification is very difficult to assess the accuracy of classification results. There is a feedback problem in adjusting the parts of pattern recognition scheme. Precise classification accuracy assessment is almost impossible to obtain, being an extremely laborious procedure. The paper presents simple neural networks for multispectral image classification, ARTMAP-like neural networks as more sophisticated tools for classification, and a modular approach to achieve the highest classification accuracy of multispectral images. There is a strong link to advances in computer technology, which gives much better conditions for modelling more sophisticated classifiers for multispectral images.

4

A Fuzzy If-Then Rule-Based Nonlinear Classifier

Łęski J.

International Journal of Applied Mathematics and Computer Science

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2003

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Vol. 13, no 2

215-223

EN

This paper introduces a new classifier design method that is based on a modification of the classical Ho-Kashyap procedure. The proposed method uses the absolute error, rather than the squared error, to design a linear classifier. Additionally, easy control of the generalization ability and robustness to outliers are obtained. Next, an extension to a nonlinear classifier by the mixture-of-experts technique is presented. Each expert is represented by a fuzzy if-then rule in the Takagi-Sugeno-Kang form. Finally, examples are given to demonstrate the validity of the introduced method.

5

Minimum absolute error classifier design with generalization control

Łęski J.

Archives of Control Sciences

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2002

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Vol. 12, no. 3

289-299

EN

This paper introduces a new classifier design method, that is based on an extension of the classical Ho-Kashyap procedure. The proposed method uses absolute error rather than square errorto design a linear classifier. Additionally, easy control of generalization ability and outliers robustness is obtained. Finally, examples are giver to demonstrate the validity of the introduced method.