{"id":2026,"date":"2016-02-07T01:14:31","date_gmt":"2016-02-07T01:14:31","guid":{"rendered":"http:\/\/web.ist.utl.pt\/rmch\/?page_id=2026"},"modified":"2024-12-18T22:58:10","modified_gmt":"2024-12-18T22:58:10","slug":"phd","status":"publish","type":"page","link":"http:\/\/web.ist.utl.pt\/rmch\/research\/publications\/phd","title":{"rendered":"phd"},"content":{"rendered":"

Learning from High-Dimensional Data (PhD Thesis)<\/font><\/h3>\n

The author is currently available for talks and to teach classes on the subjects (contact<\/a> the author for this end).
\nPlease use this bib<\/em> file<\/a> (and associated publications<\/a>) to cite the work.<\/p>\n

The contents of the thesis can be accessed below (compact format):<\/p>\n

<\/p>\n\n\n\n
  <\/td>\n\n\n\n\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n  <\/td>\n\nI-II. Foundations<\/b><\/a> 65 pages (available)<\/em><\/p>\n

I.  Introduction<\/em>
\n    Abstract, Acknowledgments and Introduction
\n    Universe of Discourse
\n    Problem Motivation
\n    Solution Space and Contents Organization
\nII. Assessing Models Learned from High-Dimensional Data<\/em>
\n    Performance Guarantees of Classification Models
\n    Performance Guarantees of Local Descriptive Models
\n    Synthetic Data Generation for Robust Assessments<\/td>\n

  <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p><\/div>\n

<\/p>\n\n\n\n
  <\/td>\n\n\n\n\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n  <\/td>\n\nIII. Learning Local Descriptive Models from Tabular Data<\/b><\/a> 151 pages (available)<\/em><\/p>\n

C1 A Structured View on Pattern-based Biclustering
\nC2 Biclustering Robust to Noise, Missings and Discretization Problems
\nC3 Additive, Multiplicative and Symmetric Models for Tabular Data
\nC4 Flexible Plaid Models: Meaningful Interactions between Biclusters
\nC5 Scalable Pattern-based Biclustering
\nC6 Flexible and Robust Order-Preserving Biclustering
\nC7 Biclustering with Efficient Closing Options
\nC8 Effective Biclustering of Large-Scale Network Data
\nC9 Flexible Pattern-based Biclustering: Putting All Together
\nC10 Constraint-based Biclustering with Domain Knowledge<\/td>\n

  <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p><\/div>\n

<\/p>\n\n\n\n
  <\/td>\n\n\n\n\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n  <\/td>\n\nIV. Learning Local Descriptive Models from Structured Data<\/b><\/a> 62 pages (provided via request, soon available here)<\/em><\/p>\n

C1 Learning Cascade Models from Multivariate Time Series
\nC2 Learning Local Descriptive Models from Multi-sets of Events
\nC3 Stochastic Modeling of Itemset Sequences
\nC4 Advanced Stochastic Modeling of Structured Data<\/td>\n

  <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p><\/div>\n

<\/p>\n\n\n\n
  <\/td>\n\n\n\n\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n  <\/td>\n\nV. Significance Guarantees of Local Descriptive Models<\/b><\/a> 42 pages (available)<\/em><\/p>\n

C1 Assessing the Statistical Significance of Biclustering Solutions
\nC2 Significance of Biclusters with Flexible Coherencies
\nC3 Assessing the Significance of Real-valued Biclusters
\nC4 Significance of Local Descriptive Models from Structured Data<\/td>\n

  <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p><\/div>\n

<\/p>\n\n\n\n
  <\/td>\n\n\n\n\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n  <\/td>\n\nVI. Learning Effective Associative Classifiers<\/b><\/a> 88 pages (available)<\/em><\/p>\n

C1 Effective Associative Classification from Discriminative Biclusters
\nC2 Classification from Regions with Non-Constant Coherency
\nC3 Advanced Aspects of Associative Classification
\nC4 Learning Associative Classifiers from Structured Data
\nC5 Classification from Statistically Significant Regions
\nC6 Learning Significant and Accurate Decisions
\nC7 Multi-period Classification for Predictive Tasks<\/td>\n

  <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p><\/div>\n

<\/p>\n\n\n\n
  <\/td>\n\n\n\n\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n  <\/td>\n\nVII. Conclusions, Future Work and Bibliography<\/b><\/a> 32 pages (available)<\/em><\/p>\n

Validation, Contributions and Implications
\nFuture Work
\nReferences<\/td>\n

  <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p><\/div>\n

For an introductory view of the covered contents in this dissertation:<\/p>\n

<\/p>\n\n\n\n
  <\/td>\n\n\n\n\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n  <\/td>\n\nSynthesis of Contents and Contributions<\/b><\/a> 35 pages (available)<\/em><\/td>\n  <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"

Learning from High-Dimensional Data (PhD Thesis) The author is currently available for talks and to teach classes on the subjects (contact the author for this end). Please use this bib file (and associated publications) to cite the work. The contents of the thesis can be accessed below (compact format): For an introductory view of the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1453,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2026","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/pages\/2026","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/comments?post=2026"}],"version-history":[{"count":45,"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/pages\/2026\/revisions"}],"predecessor-version":[{"id":2647,"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/pages\/2026\/revisions\/2647"}],"up":[{"embeddable":true,"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/pages\/1453"}],"wp:attachment":[{"href":"http:\/\/web.ist.utl.pt\/rmch\/wp-json\/wp\/v2\/media?parent=2026"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}