Construction of nonlinear discrimination function based on the MDL criterion

Manabu Sato; Mineichi Kudo; Jun Toyama; Masaru Shimbo

Displaying similar documents to “Construction of nonlinear discrimination function based on the MDL criterion”

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...

Effective Semi-supervised Nonlinear Dimensionality Reduction for Wood Defects Recognition

Zhao Zhang, Ning Ye (2010)

Computer Science and Information Systems

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Data mining methods for gene selection on the basis of gene expression arrays

Michał Muszyński, Stanisław Osowski (2014)

International Journal of Applied Mathematics and Computer Science

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Piecewise linear classifiers preserving high local recognition rates

Hiroshi Tenmoto, Mineichi Kudo, Masaru Shimbo (1998)

Kybernetika

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We propose a new method to construct piecewise linear classifiers. This method constructs hyperplanes of a piecewise linear classifier so as to keep the correct recognition rate over a threshold for a training set. The threshold is determined automatically by the MDL (Minimum Description Length) criterion so as to avoid overfitting of the classifier to the training set. The proposed method showed better results in some experiments than a previous method.

Linear discriminant analysis with a generalization of the Moore-Penrose pseudoinverse

Tomasz Górecki, Maciej Łuczak (2013)

International Journal of Applied Mathematics and Computer Science

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The Linear Discriminant Analysis (LDA) technique is an important and well-developed area of classification, and to date many linear (and also nonlinear) discrimination methods have been put forward. A complication in applying LDA to real data occurs when the number of features exceeds that of observations. In this case, the covariance estimates do not have full rank, and thus cannot be inverted. There are a number of ways to deal with this problem. In this paper, we propose improving...

Featureless pattern classification

Robert P. W. Duin, Dick de Ridder, David M. J. Tax (1998)

Kybernetika

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In this paper the possibilities are discussed for training statistical pattern recognizers based on a distance representation of the objects instead of a feature representation. Distances or similarities are used between the unknown objects to be classified with a selected subset of the training objects (the support objects). These distances are combined into linear or nonlinear classifiers. In this approach the feature definition problem is replaced by finding good similarity measures....