Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 Enriching Education through Data Mining
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   Chapter 2 Human Dynamics: From Human Mobility to Predictability
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   Chapter 3 Embracing Uncertainty: Applied Machine Learning Comes of Age
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   Chapter 4 Highly Dimensional Problems in Computational Advertising
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   Chapter 5 Learning from Constraints
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   Chapter 6 Permutation Structure in 0-1 Data
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   Chapter 7 Reading Customers Needs and Expectations with Analytics
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   Chapter 8 Algorithms and Challenges on the GeoWeb
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    Chapter 9 Data Science and Machine Learning at Scale
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    Chapter 10 Smart Cities: How Data Mining and Optimization Can Shape Future Cities
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    Chapter 11 Preference-Based Policy Learning
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    Chapter 12 Constraint Selection for Semi-supervised Topological Clustering
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    Chapter 13 Is There a Best Quality Metric for Graph Clusters?
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    Chapter 14 Adaptive Boosting for Transfer Learning Using Dynamic Updates
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    Chapter 15 Peer and Authority Pressure in Information-Propagation Models
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    Chapter 16 Constrained Logistic Regression for Discriminative Pattern Mining
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    Chapter 17 α-Clusterable Sets
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    Chapter 18 Privacy Preserving Semi-supervised Learning for Labeled Graphs
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    Chapter 19 Machine Learning and Knowledge Discovery in Databases
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    Chapter 20 A Spectral Learning Algorithm for Finite State Transducers
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    Chapter 21 An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering
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    Chapter 22 Learning Good Edit Similarities with Generalization Guarantees
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    Chapter 23 Constrained Laplacian Score for Semi-supervised Feature Selection
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    Chapter 24 COSNet: A Cost Sensitive Neural Network for Semi-supervised Learning in Graphs
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    Chapter 25 Regularized Sparse Kernel Slow Feature Analysis
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    Chapter 26 A Selecting-the-Best Method for Budgeted Model Selection
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    Chapter 27 A Robust Ranking Methodology Based on Diverse Calibration of AdaBoost
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    Chapter 28 Active Learning of Model Parameters for Influence Maximization
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    Chapter 29 Sampling Table Configurations for the Hierarchical Poisson-Dirichlet Process
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    Chapter 30 Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning
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    Chapter 31 Learning Recommendations in Social Media Systems by Weighting Multiple Relations
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    Chapter 32 Clustering Rankings in the Fourier Domain
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    Chapter 33 PerTurbo: A New Classification Algorithm Based on the Spectrum Perturbations of the Laplace-Beltrami Operator
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    Chapter 34 Datum-Wise Classification: A Sequential Approach to Sparsity
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    Chapter 35 Manifold Coarse Graining for Online Semi-supervised Learning
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    Chapter 36 Learning from Partially Annotated Sequences
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    Chapter 37 The Minimum Transfer Cost Principle for Model-Order Selection
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    Chapter 38 A Geometric Approach to Find Nondominated Policies to Imprecise Reward MDPs
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    Chapter 39 Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs
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    Chapter 40 Building Sparse Support Vector Machines for Multi-Instance Classification
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    Chapter 41 Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes
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    Chapter 42 Unsupervised Modeling of Partially Observable Environments
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    Chapter 43 Tracking Concept Change with Incremental Boosting by Minimization of the Evolving Exponential Loss
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    Chapter 44 Fast and Memory-Efficient Discovery of the Top-k Relevant Subgroups in a Reduced Candidate Space
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    Chapter 45 Linear Discriminant Dimensionality Reduction
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    Chapter 46 DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors
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    Chapter 47 Learning the Parameters of Probabilistic Logic Programs from Interpretations
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    Chapter 48 Feature Selection Stability Assessment Based on the Jensen-Shannon Divergence
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    Chapter 49 Mining Actionable Partial Orders in Collections of Sequences
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    Chapter 50 A Game Theoretic Framework for Data Privacy Preservation in Recommender Systems