Machine Learning and Data Mining in Pattern Recognition - Info and Reading Options

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Intro -- Title Page -- Preface -- Organization -- Table of Contents -- Attribute Discretization and Data Preparation -- Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization -- Introduction -- Size and Comprehensibility of Formulas -- Formula Size -- Comprehension Difficulty -- Prediction of Comprehension Difficulty -- Comprehensibility Condition -- Construction of Comprehensible Formulas -- Review of SV Discretization -- RHS Discretization -- Implementation and Computational Results -- Extension -- Summary -- References -- Selection of Subsets of Ordered Features in Machine Learning -- Introduction -- SVM - The Basis for Modifications -- Feature Selection Based on Potential Functions Combining -- The Regularization of Signal Recognition: The Principle of Decision Rule Smoothness -- Selection of Subsets of Ordering Features -- Feature Subset Selection with Taking into Account Quadratic Difference between Neighboring Weight Coefficients -- Feature Subset Selection with Taking into Account Absolute Value of Difference between Neighboring Weight Coefficients -- Experimental Results -- Conclusion -- References -- Combination of Vector Quantization and Visualization -- Introduction -- Vector Quantization and Visualization -- Self-Organizing Map and Neural Gas -- Multidimensional Scaling -- Combination of Neural Gas and Self-Organizing Map with Multidimensional Scaling -- Experimental Investigations -- Data Sets -- Dependence of Computing Time on the Number of Neurons -- Numbers of Neurons-Winners -- Quality of Quantization and Visualization -- Mapping Images -- Conclusions -- References -- Discretization of Target Attributes for Subgroup Discovery -- Introduction -- Solution Algorithm for Cluster Problem -- Implementation -- Computational Results -- Summary -- References

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