Learning from High-Dimensional Data (PhD Thesis)
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Please use this bib file (and associated publications) to cite the work.
The contents of the thesis can be accessed below (compact format):
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I. Foundations 32 pages (available) Abstract, Acknowledgments and Introduction Universe of Discourse Problem Motivation Solution Space and Contents Organization |
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II. Performance of Models Learned from High-Dimensional Data 46 pages (provided via request, soon available here) C1 Performance Guarantees of Classification Models C2 Performance Guarantees of Local Descriptive Models C3 Synthetic Data Generation for Robust Assessments |
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III. Learning Local Descriptive Models from Tabular Data 151 pages (available) C1 A Structured View on Pattern-based Biclustering C2 Biclustering Robust to Noise, Missings and Discretization Problems C3 Additive, Multiplicative and Symmetric Models for Tabular Data C4 Flexible Plaid Models: Meaningful Interactions between Biclusters C5 Scalable Pattern-based Biclustering C6 Flexible and Robust Order-Preserving Biclustering C7 Biclustering with Efficient Closing Options C8 Effective Biclustering of Large-Scale Network Data C9 Flexible Pattern-based Biclustering: Putting All Together C10 Constraint-based Biclustering with Domain Knowledge |
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IV. Learning Local Descriptive Models from Structured Data 62 pages (provided via request, soon available here) C1 Learning Cascade Models from Multivariate Time Series C2 Learning Local Descriptive Models from Multi-sets of Events C3 Stochastic Modeling of Itemset Sequences C4 Advanced Stochastic Modeling of Structured Data |
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V. Significance Guarantees of Local Descriptive Models 43 pages (provided via request, soon available here) C1 Assessing the Statistical Significance of Biclustering Solutions C2 Significance of Biclusters with Flexible Coherencies C3 Assessing the Significance of Real-valued Biclusters C4 Significance of Local Descriptive Models from Structured Data |
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VI. Learning Effective Associative Classifiers 88 pages (provided via request, soon available here) C1 Effective Associative Classification from Discriminative Biclusters C2 Classification from Regions with Non-Constant Coherency C3 Advanced Aspects of Associative Classification C4 Learning Associative Classifiers from Structured Data C5 Classification from Statistically Significant Regions C6 Learning Significant and Accurate Decisions C7 Multi-period Classification for Predictive Tasks |
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VII. Conclusions, Future Work and Bibliography 32 pages (available) Validation, Contributions and Implications Future Work References |
For an introductory view of the covered contents in this dissertation:
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Synthesis of Contents and Contributions 35 pages (available) |