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Preface; Organization; Contents; Part I Theory and Methods; A Two-Part Approach to Face Recognition: Generalized Hough Transform and Image Descriptors; 1 Introduction; 2 Method; 2.1 Modified GHT; 2.2 Gradient Distance Descriptor; 3 Results and Discussion; 4 Conclusions; References; Improved Boosting Performance by Explicit Handling of Ambiguous Positive Examples; 1 Introduction; 1.1 Relation to Bootstrapping Methods; 1.2 Contributions; 2 Relation to Previous Work; 3 Boosting Theory; 3.1 Convex-Loss Boosting Algorithms; 3.2 Robust Boosting Algorithms; 4 A Two-Pass Exclusion Extension
4.1 Inverted Cascade5 Experiments; 6 Results; 6.1 Comparison of Boosting Algorithms; 6.2 Bootstrapping Methods in Relation to Outlier Exclusion; 7 Discussion and Future Work; 8 Conclusions; References; Discriminative Dimensionality Reduction for the Visualization of Classifiers; 1 Introduction; 2 Supervised Visualization Based on the Fisher Information; 2.1 Computation of the Class Probabilities; 2.2 Approximation of Minimum Path Integrals; 3 Training a Discriminative Visualization Mapping; 4 Visualization of Classifiers; 5 Conclusions; References
Online Unsupervised Neural-Gas Learning Method for Infinite Data Streams1 Introduction; 2 Related Work; 3 Proposed Algorithm (AING); 3.1 General Behaviour; 3.2 AING Distance Threshold; 3.3 AING Merging Process; 4 Experimental Evaluation; 4.1 Experiments on Synthetic Data; 4.2 Experiments on Real Datasets; 5 Conclusions and Future Work; References; The Path Kernel: A Novel Kernel for Sequential Data; 1 Introduction; 2 Kernels and Sequences; 2.1 Sequence Similarity Measures; 3 The Path Kernel; 3.1 Efficient Computation; 3.2 Ground Kernel Choice; 4 Experiments; 5 Conclusions; References
A MAP Approach to Evidence Accumulation Clustering1 Introduction; 2 Probabilistic Model; 3 Optimization Algorithm; 3.1 Computation of a Search Direction; 3.2 Computation of an Optimal Step Size; 3.3 Complexity; 4 Related Work; 5 Experiments and Results; 5.1 UCI and Synthetic Data; 5.2 Text Data; 6 Conclusions; References; Feature Discretization with Relevance and Mutual Information Criteria; 1 Introduction; 1.1 Our Contribution; 2 Background; 2.1 Entropy and Mutual Information; 2.2 Feature Discretization; 2.3 Unsupervised Discretization; 2.4 Supervised Discretization; 3 Proposed Methods
3.1 Relevance-Based LBG3.2 Mutual Information Discretization; 4 Experimental Evaluation; 4.1 Comparison Between Our Approaches; 4.2 Comparison with Existing Methods; 5 Conclusions; References; Multiclass Semi-supervised Learning on Graphs Using Ginzburg-Landau Functional Minimization; 1 Introduction; 2 Data Segmentation with the Ginzburg-Landau Model; 2.1 Application of Diffuse Interface Models to Graphs; 3 Multiclass Extension; 3.1 Generalized Difference Function; 3.2 Computational Algorithm; 4 Results; 4.1 Synthetic Data; 4.2 Image Segmentation; 4.3 Benchmark Sets; 5 Conclusions
4.1 Inverted Cascade5 Experiments; 6 Results; 6.1 Comparison of Boosting Algorithms; 6.2 Bootstrapping Methods in Relation to Outlier Exclusion; 7 Discussion and Future Work; 8 Conclusions; References; Discriminative Dimensionality Reduction for the Visualization of Classifiers; 1 Introduction; 2 Supervised Visualization Based on the Fisher Information; 2.1 Computation of the Class Probabilities; 2.2 Approximation of Minimum Path Integrals; 3 Training a Discriminative Visualization Mapping; 4 Visualization of Classifiers; 5 Conclusions; References
Online Unsupervised Neural-Gas Learning Method for Infinite Data Streams1 Introduction; 2 Related Work; 3 Proposed Algorithm (AING); 3.1 General Behaviour; 3.2 AING Distance Threshold; 3.3 AING Merging Process; 4 Experimental Evaluation; 4.1 Experiments on Synthetic Data; 4.2 Experiments on Real Datasets; 5 Conclusions and Future Work; References; The Path Kernel: A Novel Kernel for Sequential Data; 1 Introduction; 2 Kernels and Sequences; 2.1 Sequence Similarity Measures; 3 The Path Kernel; 3.1 Efficient Computation; 3.2 Ground Kernel Choice; 4 Experiments; 5 Conclusions; References
A MAP Approach to Evidence Accumulation Clustering1 Introduction; 2 Probabilistic Model; 3 Optimization Algorithm; 3.1 Computation of a Search Direction; 3.2 Computation of an Optimal Step Size; 3.3 Complexity; 4 Related Work; 5 Experiments and Results; 5.1 UCI and Synthetic Data; 5.2 Text Data; 6 Conclusions; References; Feature Discretization with Relevance and Mutual Information Criteria; 1 Introduction; 1.1 Our Contribution; 2 Background; 2.1 Entropy and Mutual Information; 2.2 Feature Discretization; 2.3 Unsupervised Discretization; 2.4 Supervised Discretization; 3 Proposed Methods
3.1 Relevance-Based LBG3.2 Mutual Information Discretization; 4 Experimental Evaluation; 4.1 Comparison Between Our Approaches; 4.2 Comparison with Existing Methods; 5 Conclusions; References; Multiclass Semi-supervised Learning on Graphs Using Ginzburg-Landau Functional Minimization; 1 Introduction; 2 Data Segmentation with the Ginzburg-Landau Model; 2.1 Application of Diffuse Interface Models to Graphs; 3 Multiclass Extension; 3.1 Generalized Difference Function; 3.2 Computational Algorithm; 4 Results; 4.1 Synthetic Data; 4.2 Image Segmentation; 4.3 Benchmark Sets; 5 Conclusions