Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 Classifying a Stream of Infinite Concepts: A Bayesian Non-parametric Approach
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   Chapter 2 Fast Nearest Neighbor Search on Large Time-Evolving Graphs
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   Chapter 3 Deconstructing Kernel Machines
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   Chapter 4 Beyond Blocks: Hyperbolic Community Detection
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   Chapter 5 Concurrent Bandits and Cognitive Radio Networks
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   Chapter 6 Kernel Principal Geodesic Analysis
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   Chapter 7 Attributed Graph Kernels Using the Jensen-Tsallis q-Differences
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   Chapter 8 Sub-sampling for Multi-armed Bandits
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    Chapter 9 Knowledge-Powered Deep Learning for Word Embedding
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    Chapter 10 Density-Based Subspace Clustering in Heterogeneous Networks
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    Chapter 11 Open Question Answering with Weakly Supervised Embedding Models
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    Chapter 12 Towards Automatic Feature Construction for Supervised Classification
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    Chapter 13 Consistency of Losses for Learning from Weak Labels
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    Chapter 14 Active Learning for Support Vector Machines with Maximum Model Change
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    Chapter 15 Anomaly Detection with Score Functions Based on the Reconstruction Error of the Kernel PCA
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    Chapter 16 Fast Gaussian Pairwise Constrained Spectral Clustering
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    Chapter 17 A Generative Bayesian Model for Item and User Recommendation in Social Rating Networks with Trust Relationships
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    Chapter 18 Domain Adaptation with Regularized Optimal Transport
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    Chapter 19 Discriminative Subnetworks with Regularized Spectral Learning for Global-State Network Data
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    Chapter 20 Infinitely Many-Armed Bandits with Unknown Value Distribution
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    Chapter 21 Cautious Ordinal Classification by Binary Decomposition
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    Chapter 22 Error-Bounded Approximations for Infinite-Horizon Discounted Decentralized POMDPs
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    Chapter 23 Approximate Consistency: Towards Foundations of Approximate Kernel Selection
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    Chapter 24 Nowcasting with Numerous Candidate Predictors
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    Chapter 25 Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries
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    Chapter 26 FASOLE: Fast Algorithm for Structured Output LEarning
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    Chapter 27 Neutralized Empirical Risk Minimization with Generalization Neutrality Bound
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    Chapter 28 Effective Blending of Two and Three-way Interactions for Modeling Multi-relational Data
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    Chapter 29 Automatic Design of Neuromarkers for OCD Characterization
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    Chapter 30 Importance Weighted Inductive Transfer Learning for Regression
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    Chapter 31 Policy Search for Path Integral Control
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    Chapter 32 How Many Topics? Stability Analysis for Topic Models
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    Chapter 33 Joint Prediction of Topics in a URL Hierarchy
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    Chapter 34 Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
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    Chapter 35 Combination of One-Class Support Vector Machines for Classification with Reject Option
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    Chapter 36 Relative Comparison Kernel Learning with Auxiliary Kernels
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    Chapter 37 Transductive Minimax Probability Machine
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    Chapter 38 Covariate-Correlated Lasso for Feature Selection
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    Chapter 39 Random Forests with Random Projections of the Output Space for High Dimensional Multi-label Classification
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    Chapter 40 Communication-Efficient Distributed Online Prediction by Dynamic Model Synchronization
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    Chapter 41 Hetero-Labeled LDA: A Partially Supervised Topic Model with Heterogeneous Labels
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    Chapter 42 Bayesian Multi-view Tensor Factorization
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    Chapter 43 Conditional Log-linear Models for Mobile Application Usage Prediction
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    Chapter 44 Pushing-Down Tensor Decompositions over Unions to Promote Reuse of Materialized Decompositions