"Machine Learning and Knowledge Discovery in Databases" - Information and Links:

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European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II

"Machine Learning and Knowledge Discovery in Databases" was published by Springer in 2015 - Cham, it has 1 pages and the language of the book is English.


“Machine Learning and Knowledge Discovery in Databases” Metadata:

  • Title: ➤  Machine Learning and Knowledge Discovery in Databases
  • Authors:
  • Language: English
  • Number of Pages: 1
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Cham
  • Library of Congress Classification: QA75.5-76.95

“Machine Learning and Knowledge Discovery in Databases” Subjects and Themes:

Edition Specifications:

  • Pagination: 773

Edition Identifiers:

AI-generated Review of “Machine Learning and Knowledge Discovery in Databases”:


"Machine Learning and Knowledge Discovery in Databases" Description:

Open Data:

Intro -- Preface -- Organization -- Abstracts of Journal Track Articles -- Contents - Part II -- Research Track Matrix and Tensor Analysis -- BoostMF: Boosted Matrix Factorisation for Collaborative Ranking -- 1 Introduction -- 2 Related Work -- 3 Boosted Matrix Factorisation (BoostMF) -- 3.1 Probabilistic Matrix Factorisation (PMF) -- 3.2 BoostMF -- 3.3 Theoretical Analysis -- 4 Experiments -- 4.1 Datasets and Evaluation Metric -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Convex Factorization Machines -- 1 Introduction -- 2 Factorization Machines -- 3 Convex Formulation -- 4 Optimization Algorithm -- 4.1 Minimizing with Respect to bold0mu mumu ww2005/06/28 ver: 1.3 subfig packagewwww -- 4.2 Minimizing with Respect to bold0mu mumu ZZ2005/06/28 ver: 1.3 subfig packageZZZZ -- 4.3 Squared Loss Case -- 4.4 Computational Complexity -- 4.5 Convergence Guarantees -- 5 Experimental Results -- 5.1 Synthetic Experiments -- 5.2 Recommender System Experiments -- 6 Related Work -- 7 Conclusion -- References -- Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond Blocks -- 1 Introduction -- 2 Related Work -- 3 Definitions -- 3.1 Generalized Outer Product -- 3.2 Generalized Rank -- 4 Computational Complexity -- 4.1 Rank-1 Submatrices -- 4.2 Selecting Some Rank-1 Submatrices -- 4.3 Minimum-Error Sub-Decompositions -- 4.4 Deciding the Rank -- 4.5 Minimum-Error Approximate Decompositions -- 5 Approximability -- 5.1 Approximating Smallest Sub-Decompositions -- 5.2 Approximating Minimum-Error Sub-Decompositions -- 6 Conclusions and Future Work -- References -- Scalable Bayesian Non-negative Tensor Factorization for Massive Count Data -- 1 Introduction -- 2 Canonical PARAFAC Decomposition -- 3 Beta-Negative Binomial CP Decomposition -- 3.1 Reparametrizing the Poisson Distribution

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