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

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Intro -- Preface -- Organization -- Contents - Part II -- Supervised Learning -- Exploiting the Earth's Spherical Geometry to Geolocate Images -- 1 Introduction -- 2 Prior Work -- 2.1 Image Retrieval -- 2.2 Classification -- 3 Geolocation via the MvMF -- 3.1 The Probabilistic Interpretation -- 3.2 Interpretation as a Classifier -- 3.3 Interpretation as an Image Retrieval Method -- 3.4 Analysis -- 4 Experiments -- 4.1 Procedure -- 4.2 Results -- 5 Conclusion -- References -- Continual Rare-Class Recognition with Emerging Novel Subclasses -- 1 Introduction -- 2 Problem Setup and Preliminary Data Analysis -- 3 Continual Rare-Class Recognition -- 3.1 Model Formulation -- 3.2 Convexity and Optimization -- 3.3 Time and Space-Complexity Analysis -- 4 Evaluation -- 4.1 Experiment Setup -- 4.2 Experiment Results -- 5 Related Work -- 6 Conclusion -- References -- Unjustified Classification Regions and Counterfactual Explanations in Machine Learning -- 1 Introduction -- 2 Background -- 2.1 Post-hoc Interpretability -- 2.2 Studies of Post-hoc Interpretability Approaches -- 2.3 Adversarial Examples -- 3 Justification Using Ground-Truth Data -- 3.1 Intuition and Definitions -- 3.2 Implementation -- 4 Procedures for Assessing the Risk of Unconnectedness -- 4.1 LRA Procedure -- 4.2 VE Procedure -- 5 Experimental Study: Assessing the Risk of Unjustified Regions -- 5.1 Experimental Protocol -- 5.2 Defining the Problem Granularity: Choosing n and -- 5.3 Detecting Unjustified Regions -- 5.4 Vulnerability of Post-hoc Counterfactual Approaches -- 6 Conclusion -- References -- Shift Happens: Adjusting Classifiers -- 1 Introduction -- 2 Background and Related Work -- 2.1 Dataset Shift and Prior Probability Adjustment -- 2.2 Proper Scoring Rules and Bregman Divergences -- 2.3 Adjusted Predictions and Adjustment Procedures -- 3 General Adjustment

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