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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
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    Chapter 1 AR-Boost: Reducing Overfitting by a Robust Data-Driven Regularization Strategy
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    Chapter 2 Parallel Boosting with Momentum
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    Chapter 3 Inner Ensembles: Using Ensemble Methods Inside the Learning Algorithm
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    Chapter 4 Learning Discriminative Sufficient Statistics Score Space for Classification
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    Chapter 5 The Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited
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    Chapter 6 Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression
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    Chapter 7 MORD: Multi-class Classifier for Ordinal Regression
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    Chapter 8 Identifiability of Model Properties in Over-Parameterized Model Classes
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    Chapter 9 Exploratory Learning
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    Chapter 10 Semi-supervised Gaussian Process Ordinal Regression
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    Chapter 11 Influence of Graph Construction on Semi-supervised Learning
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    Chapter 12 Tractable Semi-supervised Learning of Complex Structured Prediction Models
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    Chapter 13 PSSDL: Probabilistic Semi-supervised Dictionary Learning
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    Chapter 14 Embedding with Autoencoder Regularization
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    Chapter 15 Reduced-Rank Local Distance Metric Learning
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    Chapter 16 Learning Exemplar-Represented Manifolds in Latent Space for Classification
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    Chapter 17 Locally Linear Landmarks for Large-Scale Manifold Learning
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    Chapter 18 Discovering Skylines of Subgroup Sets
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    Chapter 19 Difference-Based Estimates for Generalization-Aware Subgroup Discovery
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    Chapter 20 Local Outlier Detection with Interpretation
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    Chapter 21 Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines
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    Chapter 22 Mining Outlier Participants: Insights Using Directional Distributions in Latent Models
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    Chapter 23 Anonymizing Data with Relational and Transaction Attributes
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    Chapter 24 Privacy-Preserving Mobility Monitoring Using Sketches of Stationary Sensor Readings
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    Chapter 25 Evasion Attacks against Machine Learning at Test Time
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    Chapter 26 The Top-k Frequent Closed Itemset Mining Using Top-k SAT Problem
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    Chapter 27 A Declarative Framework for Constrained Clustering
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    Chapter 28 SNNAP: Solver-Based Nearest Neighbor for Algorithm Portfolios
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    Chapter 29 Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals
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    Chapter 30 Incremental Sensor Placement Optimization on Water Network
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    Chapter 31 Detecting Marionette Microblog Users for Improved Information Credibility
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    Chapter 32 Will My Question Be Answered? Predicting “Question Answerability” in Community Question-Answering Sites
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    Chapter 33 Learning to Detect Patterns of Crime
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    Chapter 34 Space Allocation in the Retail Industry: A Decision Support System Integrating Evolutionary Algorithms and Regression Models
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    Chapter 35 Forest-Based Point Process for Event Prediction from Electronic Health Records
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    Chapter 36 On Discovering the Correlated Relationship between Static and Dynamic Data in Clinical Gait Analysis
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    Chapter 37 Computational Drug Repositioning by Ranking and Integrating Multiple Data Sources
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    Chapter 38 Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.
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    Chapter 39 A Theoretical Framework for Exploratory Data Mining: Recent Insights and Challenges Ahead
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    Chapter 40 Tensor Factorization for Multi-relational Learning
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    Chapter 41 MONIC and Followups on Modeling and Monitoring Cluster Transitions
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    Chapter 42 Towards Robot Skill Learning: From Simple Skills to Table Tennis
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    Chapter 43 Functional MRI Analysis with Sparse Models
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    Chapter 44 Image Hub Explorer: Evaluating Representations and Metrics for Content-Based Image Retrieval and Object Recognition
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    Chapter 45 Ipseity – A Laboratory for Synthesizing and Validating Artificial Cognitive Systems in Multi-agent Systems
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    Chapter 46 OpenML: A Collaborative Science Platform
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    Chapter 47 ViperCharts: Visual Performance Evaluation Platform
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    Chapter 48 Entityclassifier.eu: Real-Time Classification of Entities in Text with Wikipedia
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    Chapter 49 Hermoupolis: A Trajectory Generator for Simulating Generalized Mobility Patterns
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    Chapter 50 AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data
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    Chapter 51 ScienScan – An Efficient Visualization and Browsing Tool for Academic Search
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    Chapter 52 InVis: A Tool for Interactive Visual Data Analysis
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    Chapter 53 Kanopy: Analysing the Semantic Network around Document Topics
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    Chapter 54 SCCQL : A Constraint-Based Clustering System
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    Chapter 55 Erratum: Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals
Overall attention for this book and its chapters
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Mentioned by

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1 blog
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2 patents
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2 Facebook pages
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7 Wikipedia pages

Citations

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Title
Machine Learning and Knowledge Discovery in Databases
Published by
Springer, Berlin, Heidelberg, January 2013
DOI 10.1007/978-3-642-40994-3
ISBNs
978-3-64-240993-6, 978-3-64-240994-3
Editors

Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Železný

X Demographics

X Demographics

The data shown below were collected from the profiles of 50 X users 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 36%
Researcher 4 18%
Student > Bachelor 2 9%
Student > Doctoral Student 2 9%
Professor 2 9%
Other 5 23%
Readers by discipline Count As %
Computer Science 10 45%
Engineering 5 23%
Mathematics 2 9%
Agricultural and Biological Sciences 1 5%
Earth and Planetary Sciences 1 5%
Other 3 14%