|
F. Aherne, N. Thacker and P. Rockett
|
The Bhattacharyya metric as an absolute similarity measure for frequency coded data |
|
E. Alpaydin and C. Kaynak
|
Cascading classifiers |
|
V. Brailovsky and M. Har-Even
|
Detecting a data set structure through the use of nonlinear projections search and optimization |
|
M. van Breukelen, R. Duin, D. Tax and J. Hartog
|
Handwritten digit recognition by combined classifiers |
|
L. Bruzzone and S. Serpico
|
A simple upper bound to the Bayes error probability for feature selection |
|
M. Dang and G. Govaert
|
Fuzzy clustering of spatial binary data |
|
R. Duin, D. de Ridder and D. Tax
|
Featureless pattern classification |
| F. Ferri |
Combining adaptive vector quantization and prototype selection techniques to improve nearest neighbour classifiers |
|
J. Flusser and T. Suk
|
On selecting the best features in a noisy environment |
| J. Grim |
Mixture of experts architectures for neural networks as a special case of conditional expectation formula |
|
M. Haindl and S. Šimberová
|
A scratch removal method |
|
M. Kudo and J. Sklansky
|
A comparative evaluation of medium- and large-scale feature selectors for pattern classifiers |
|
R. Kumar and P. Rockett
|
Decomposition of high dimensional pattern spaces for hierarchical classification |
| E. Nyssen |
Interpretation of pattern classification results, obtained from a test set |
| J. Pik |
Transformation of structural patterns in discrete events? An application of structural methods in discrete event systems |
|
P. Pudil, J. Novovičová, P. Somol and R. Vrňata
|
Conceptual base of feature selection consulting system |
| Š. Raudys |
Intrinsic dimensionality and small sample properties of classifiers |
|
M. Sato, M. Kudo, J. Toyama and M. Shimbo
|
Construction of nonlinear discrimination function based on the MDL criterion |
| L. Soukup |
Probability distribution of transformed random variables with application to nonlinear features extraction |
|
H. Tenmoto, M. Kudo and M. Shimbo
|
Piecewise linear classifiers preserving high local recognition rates |
|
I. Vajda and J. Grim
|
About the maximum information and maximum likelihood principles |