Machine Learning and Knowledge Discovery in Databases - Info and Reading Options

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Intro -- Preface -- Organization -- Invited Talks Abstracts -- Scalable Collective Reasoning Using Probabilistic Soft Logic -- Network Analysis in the Big Data Age: Mining Graph and Social Streams -- Big Data for Personalized Medicine: A Case Study of Biomarker Discovery -- Machine Learning for Search Ranking and Ad Auction -- Beyond Stochastic Gradient Descent for Large-Scale Machine Learning -- Industrial Invited Talks Abstracts -- Making Smart Metering Smarter by Applying Data Analytics -- Ads That Matter -- Machine Learning and Data Mining in Call of Duty -- Algorithms, Evolution and Network-Based Approaches in Molecular Discovery -- Table of Contents - Part III -- Main Track Contributions -- FLIP: Active Learning for Relational Network Classification -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Definition and Notations -- 3.2 Active Learning Using Iterative Classification Algorithm -- 3.3 Active Learning for Multi-labeled Networks -- 3.4 Variants of FLIP for Single- and Multi-labeled Networks -- 3.5 Variants of FLIP-per-label for Multi-labeled Networks -- 4 Experimental Protocol -- 4.1 Datasets -- 4.2 Comparative Methods -- 4.3 Validation Protocol -- 5 Results and Discussion -- 5.1 Active Learning for Single-Labeled Networks -- 5.2 Active Learning for Multi-labeled Networks -- 6 Conclusion -- References -- Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density -- 1 Introduction -- 2 Direct Estimation of the Gradient of a Log-Density -- 2.1 Problem Formulation -- 2.2 Least-Squares Log-Density Gradient -- 2.3 Model Selection by Cross-Validation -- 3 Clustering via Mode Seeking -- 3.1 Gradient-Based Approaches -- 3.2 Fixed-Point Approach -- 4 Extensions -- 4.1 Common Basis Functions -- 4.2 Multi-task Learning -- 4.3 Sparse Estimation -- 4.4 Bregman Loss -- 4.5 Blurring Mean Shift -- 5 Experiments

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