Volume 35 (1999)

  • Volume 34

  • (1998)


Contents of volume 34, no. 4
 
F. J. Aherne, N. A. Thacker and P. Rockett The Bhattacharyya Metric as an Absolute Similarity Measure for Frequency Coded Data.
E. Alpaydin and C. Kaynak Cascading Classifiers.
V. L. Brailovsky and M. Har-Even Detecting a Data Set Structure Through the Use of Nonlinear Projections Search and Optimization.
M. van Breukelen, J. E. den Hartog, R. P. W. Duin and D. M. J. Tax Handwritten Digit Recognition by Combined Classifiers.
L. Bruzzone and S. B. 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. P. W. Duin, D. de Ridder and D. M. J. Tax Featureless Pattern Classification.
F. J. 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.
S. Raudys Intrinsic Dimensionality and Small Sample Properties of Classifiers.
M. Sato, M. Kudo, J. Toyama and M. Shimbo Construction of Nonlinear Discrimination Function Base 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.

Reviews in volume 34, no. 4
 
Y. Sato, L. C. Jain and M. Sato: Fuzzy Clustering Models and Applications.   Reviewed by: M. Mareš

Volume 33 (1997)