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Machine Learning and Knowledge Discovery in Databases

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Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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
Attention for Chapter 7: Laplacian Hamiltonian Monte Carlo
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Chapter title
Laplacian Hamiltonian Monte Carlo
Chapter number 7
Book title
Machine Learning and Knowledge Discovery in Databases
Published in
Lecture notes in computer science, September 2016
DOI 10.1007/978-3-319-46128-1_7
Book ISBNs
978-3-31-946127-4, 978-3-31-946128-1
Authors

Yizhe Zhang, Changyou Chen, Ricardo Henao, Lawrence Carin, Zhang, Yizhe, Chen, Changyou, Henao, Ricardo, Carin, Lawrence

Editors

Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 20%
Researcher 1 20%
Other 1 20%
Unknown 2 40%
Readers by discipline Count As %
Computer Science 3 60%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 November 2021.
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#20,341,859
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#6,991
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