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Machine Learning and Knowledge Discovery in Databases

Overview of attention for book
Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations
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    Chapter 2 Unsupervised Trajectory Sampling
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    Chapter 3 Fast Extraction of Locally Optimal Patterns Based on Consistent Pattern Function Variations
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    Chapter 4 Machine Learning and Knowledge Discovery in Databases
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    Chapter 5 Learning with Ensembles of Randomized Trees : New Insights
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    Chapter 6 Entropy and Margin Maximization for Structured Output Learning
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    Chapter 7 Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms
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    Chapter 8 Adapting Decision DAGs for Multipartite Ranking
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    Chapter 9 Fast and Scalable Algorithms for Semi-supervised Link Prediction on Static and Dynamic Graphs
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    Chapter 10 Modeling Relations and Their Mentions without Labeled Text
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    Chapter 11 An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery
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    Chapter 12 Selecting Information Diffusion Models over Social Networks for Behavioral Analysis
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    Chapter 13 Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach
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    Chapter 14 Online Structural Graph Clustering Using Frequent Subgraph Mining
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    Chapter 15 Large-Scale Support Vector Learning with Structural Kernels
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    Chapter 16 Synchronization Based Outlier Detection
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    Chapter 17 Laplacian Spectrum Learning
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    Chapter 18 k-Version-Space Multi-class Classification Based on k-Consistency Tests
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    Chapter 19 Complexity Bounds for Batch Active Learning in Classification
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    Chapter 20 Semi-supervised Projection Clustering with Transferred Centroid Regularization
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    Chapter 21 Permutation Testing Improves Bayesian Network Learning
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    Chapter 22 Example-dependent Basis Vector Selection for Kernel-Based Classifiers
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    Chapter 23 Surprising Patterns for the Call Duration Distribution of Mobile Phone Users
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    Chapter 24 Variational Bayesian Mixture of Robust CCA Models
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    Chapter 25 Adverse Drug Reaction Mining in Pharmacovigilance Data Using Formal Concept Analysis
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    Chapter 26 Topic Models Conditioned on Relations
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    Chapter 27 Shift-Invariant Grouped Multi-task Learning for Gaussian Processes
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    Chapter 28 Nonparametric Bayesian Clustering Ensembles
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    Chapter 29 Directed Graph Learning via High-Order Co-linkage Analysis
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    Chapter 30 Incorporating Domain Models into Bayesian Optimization for RL
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    Chapter 31 Efficient and Numerically Stable Sparse Learning
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    Chapter 32 Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes
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    Chapter 33 Many-to-Many Graph Matching: A Continuous Relaxation Approach
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    Chapter 34 Competitive Online Generalized Linear Regression under Square Loss
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    Chapter 35 Cross Validation Framework to Choose amongst Models and Datasets for Transfer Learning
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    Chapter 36 Fast, Effective Molecular Feature Mining by Local Optimization
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    Chapter 37 AnswerArt - Contextualized Question Answering
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    Chapter 38 Real-Time News Recommender System
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    Chapter 39 Machine Learning and Knowledge Discovery in Databases
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    Chapter 40 NewsGist: A Multilingual Statistical News Summarizer
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    Chapter 41 QUEST: Query Expansion Using Synonyms over Time
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    Chapter 42 Flu Detector - Tracking Epidemics on Twitter
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    Chapter 43 X-SDR: An Extensible Experimentation Suite for Dimensionality Reduction
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    Chapter 44 SOREX: Subspace Outlier Ranking Exploration Toolkit
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    Chapter 45 KDTA: Automated Knowledge-Driven Text Annotation
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    Chapter 46 Machine Learning and Knowledge Discovery in Databases
  48. Altmetric Badge
    Chapter 47 Experience STORIES: A Visual News Search and Summarization System
  49. Altmetric Badge
    Chapter 48 Exploring Real Mobility Data with M-Atlas
Overall attention for this book and its chapters
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

3 tweeters

Readers on

13 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Machine Learning and Knowledge Discovery in Databases
Published by
ADS, August 2010
DOI 10.1007/978-3-642-15939-8
978-3-64-215938-1, 978-3-64-215939-8

Balcázar, José Luis, Bonchi, Francesco, Gionis, Aristides, Sebag, Michèle

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 15%
Researcher 2 15%
Unknown 9 69%
Readers by discipline Count As %
Mathematics 2 15%
Computer Science 1 8%
Physics and Astronomy 1 8%
Unknown 9 69%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 July 2020.
All research outputs
of 15,606,134 outputs
Outputs from ADS
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Outputs of similar age
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Outputs of similar age from ADS
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Altmetric has tracked 15,606,134 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,555 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,082 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 189 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.