Machine Learning and Knowledge Discovery in Databases

Table of Contents

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   Book Overview
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   Chapter 1 adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs
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   Chapter 2 Semi-supervised Tensor Factorization for Brain Network Analysis
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   Chapter 3 Scalable Hyperparameter Optimization with Products of Gaussian Process Experts
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   Chapter 4 Incremental Commute Time Using Random Walks and Online Anomaly Detection
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   Chapter 5 Online Density Estimation of Heterogeneous Data Streams in Higher Dimensions
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   Chapter 6 Graphical Model Sketch
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   Chapter 7 Laplacian Hamiltonian Monte Carlo
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   Chapter 8 Functional Bid Landscape Forecasting for Display Advertising
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    Chapter 9 Maximizing Time-Decaying Influence in Social Networks
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    Chapter 10 Learning Beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
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    Chapter 11 Efficient Bayesian Maximum Margin Multiple Kernel Learning
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    Chapter 12 Transactional Tree Mining
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    Chapter 13 Two-Stage Transfer Surrogate Model for Automatic Hyperparameter Optimization
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    Chapter 14 Gaussian Process Pseudo-Likelihood Models for Sequence Labeling
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    Chapter 15 \(\mathtt {OSL}\alpha \) : Online Structure Learning Using Background Knowledge Axiomatization
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    Chapter 16 Beyond the Boundaries of SMOTE
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    Chapter 17 M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees
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    Chapter 18 Uncovering Locally Discriminative Structure for Feature Analysis
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    Chapter 19 Multi-Objective Group Discovery on the Social Web
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    Chapter 20 Sequential Data Classification in the Space of Liquid State Machines
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    Chapter 21 Semigeometric Tiling of Event Sequences
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    Chapter 22 Attribute Conjunction Learning with Recurrent Neural Network
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    Chapter 23 Efficient Discovery of Sets of Co-occurring Items in Event Sequences
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    Chapter 24 Collaborative Expert Recommendation for Community-Based Question Answering
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    Chapter 25 Link Prediction in Dynamic Networks Using Graphlet
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    Chapter 26 CHADE: Metalearning with Classifier Chains for Dynamic Combination of Classifiers
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    Chapter 27 Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering
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    Chapter 28 Joint Learning of Entity Semantics and Relation Pattern for Relation Extraction
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    Chapter 29 BASS: A Bootstrapping Approach for Aligning Heterogenous Social Networks
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    Chapter 30 Multi-graph Clustering Based on Interior-Node Topology with Applications to Brain Networks
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    Chapter 31 Interactive Learning from Multiple Noisy Labels
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    Chapter 32 An Online Gibbs Sampler Algorithm for Hierarchical Dirichlet Processes Prior
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    Chapter 33 Structure Pattern Analysis and Cascade Prediction in Social Networks
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    Chapter 34 Graph-Margin Based Multi-label Feature Selection
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    Chapter 35 Pure Exploration for Max-Quantile Bandits
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    Chapter 36 A Hybrid Knowledge Discovery Approach for Mining Predictive Biomarkers in Metabolomic Data
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    Chapter 37 Native Advertisement Selection and Allocation in Social Media Post Feeds
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    Chapter 38 Asynchronous Feature Extraction for Large-Scale Linear Predictors
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    Chapter 39 F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets
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    Chapter 40 Cost-Aware Early Classification of Time Series
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    Chapter 41 The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications
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    Chapter 42 On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent
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    Chapter 43 Composite Denoising Autoencoders
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    Chapter 44 Trust Hole Identification in Signed Networks
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    Chapter 45 Huber-Norm Regularization for Linear Prediction Models
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    Chapter 46 Robust Dictionary Learning on the Hilbert Sphere in Kernel Feature Space
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    Chapter 47 Is Attribute-Based Zero-Shot Learning an Ill-Posed Strategy?
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    Chapter 48 Stochastic CoSaMP: Randomizing Greedy Pursuit for Sparse Signal Recovery
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    Chapter 49 Deep Metric Learning with Data Summarization
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    Chapter 50 Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition