Artificial Neural Networks in Pattern Recognition - Info and Reading Options

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Intro -- Preface -- Organization -- Contents -- Invited Papers -- Learning Sequential Data with the Help of Linear Systems -- 1 Introduction -- 2 Computational Models -- 2.1 Learning Task -- 2.2 Linear and Non-linear Models -- 2.3 Training Approaches -- 3 Experimental Assessment -- 3.1 Results of Approaches Using Unsupervised Projections -- 3.2 Results of Approaches Using Supervision and Pre-training -- 3.3 Discussion -- 4 Conclusion -- References -- A Spiking Neural Network for Personalised Modelling of Electrogastrography (EGG) -- Abstract -- 1 Introduction -- 2 Background -- 3 Investigation -- 4 Results and Discussion -- 4.1 Implications of This Research -- 5 Conclusion -- References -- Learning Algorithms and Architectures -- Improving Generalization Abilities of Maximal Average Margin Classifiers -- 1 Introduction -- 2 Maximum Average Margin Classifiers -- 2.1 Architecture -- 2.2 Problems with MAMCs -- 2.3 Bias Term Optimization -- 2.4 Extension of MAMCs -- 2.5 Equality-Constrained MAMCs -- 3 Performance Evaluation -- 3.1 Experimental Conditions -- 3.2 Results -- 3.3 Discussions -- 4 Conclusions -- References -- Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation -- 1 Introduction -- 2 Random Fourier Features -- 3 Finding Small Sets of Random Fourier Features -- 4 Nyström Approximated Matrix Processing -- 5 Experiments -- 6 Conclusions -- References -- Incremental Construction of Low-Dimensional Data Representations -- Abstract -- 1 Introduction -- 2 Tangent Bundle Manifold Learning -- 2.1 Definitions and Assumptions -- 2.2 Tangent Bundle Manifold Learning Definition -- 2.3 Grassmann&amp -- Stiefel Eigenmaps: An Approach -- 2.4 Grassmann&amp -- Stiefel Eigenmaps: Some Details -- 2.5 Grassmann&amp -- Stiefel Eigenmaps: Some Properties -- 3 Incremental Grassmann&amp -- Stiefel Eigenmaps

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