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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 FLIP: Active Learning for Relational Network Classification
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    Chapter 2 Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density
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    Chapter 3 Local Policy Search in a Convex Space and Conservative Policy Iteration as Boosted Policy Search
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    Chapter 4 Code You Are Happy to Paste: An Algorithmic Dictionary of Exponential Families
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    Chapter 5 Statistical Hypothesis Testing in Positive Unlabelled Data
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    Chapter 6 Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model
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    Chapter 7 Gaussian Process Multi-task Learning Using Joint Feature Selection
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    Chapter 8 Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning
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    Chapter 9 Scalable Information Flow Mining in Networks
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    Chapter 10 Link Prediction in Multi-modal Social Networks
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    Chapter 11 Faster Way to Agony
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    Chapter 12 Speeding Up Recovery from Concept Drifts
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    Chapter 13 Training Restricted Boltzmann Machines with Overlapping Partitions
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    Chapter 14 Integer Bayesian Network Classifiers
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    Chapter 15 Multi-target Regression via Random Linear Target Combinations
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    Chapter 16 Ratio-Based Multiple Kernel Clustering
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    Chapter 17 Evidence-Based Clustering for Scalable Inference in Markov Logic
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    Chapter 18 On Learning Matrices with Orthogonal Columns or Disjoint Supports
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    Chapter 19 Scalable Moment-Based Inference for Latent Dirichlet Allocation
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    Chapter 20 Unsupervised Feature Selection via Unified Trace Ratio Formulation and K -means Clustering (TRACK)
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    Chapter 21 On the Equivalence between Deep NADE and Generative Stochastic Networks
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    Chapter 22 Scalable Nonnegative Matrix Factorization with Block-wise Updates
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    Chapter 23 Convergence of Min-Sum-Min Message-Passing for Quadratic Optimization
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    Chapter 24 Clustering Image Search Results by Entity Disambiguation
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    Chapter 25 Accelerating Model Selection with Safe Screening for L 1 -Regularized L 2 -SVM
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    Chapter 26 Kernel Alignment Inspired Linear Discriminant Analysis
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    Chapter 27 Transfer Learning with Multiple Sources via Consensus Regularized Autoencoders
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    Chapter 28 Branty: A Social Media Ranking Tool for Brands
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    Chapter 29 MinUS : Mining User Similarity with Trajectory Patterns
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    Chapter 30 KnowNow: A Serendipity-Based Educational Tool for Learning Time-Linked Knowledge
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    Chapter 31 Khiops CoViz: A Tool for Visual Exploratory Analysis of k -Coclustering Results
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    Chapter 32 PYTHIA: Employing Lexical and Semantic Features for Sentiment Analysis
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    Chapter 33 Spá: A Web-Based Viewer for Text Mining in Evidence Based Medicine
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    Chapter 34 Propositionalization Online
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    Chapter 35 Interactive Medical Miner : Interactively Exploring Subpopulations in Epidemiological Datasets
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    Chapter 36 WebDR: A Web Workbench for Data Reduction
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    Chapter 37 GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series
  39. Altmetric Badge
    Chapter 38 Machine Learning and Knowledge Discovery in Databases
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    Chapter 39 BestTime: Finding Representatives in Time Series Datasets
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    Chapter 40 BMaD – A Boolean Matrix Decomposition Framework
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    Chapter 41 Analyzing and Grounding Social Interaction in Online and Offline Networks
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    Chapter 42 Be Certain of How-to before Mining Uncertain Data
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    Chapter 43 Active Learning Is Planning: Nonmyopic ε -Bayes-Optimal Active Learning of Gaussian Processes
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    Chapter 44 Generalized Online Sparse Gaussian Processes with Application to Persistent Mobile Robot Localization
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    Chapter 45 Distributional Clauses Particle Filter
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    Chapter 46 Network Reconstruction for the Identification of miRNA:mRNA Interaction Networks
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    Chapter 47 Machine Learning Approaches for Metagenomics
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    Chapter 48 Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies
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    Chapter 49 Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights
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    Chapter 50 Agents Teaching Agents in Reinforcement Learning (Nectar Abstract)
Attention for Chapter 15: Multi-target Regression via Random Linear Target Combinations
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

5 X users
1 Facebook page


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Readers on

58 Mendeley
1 CiteULike
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Chapter title
Multi-target Regression via Random Linear Target Combinations
Chapter number 15
Book title
Machine Learning and Knowledge Discovery in Databases
Published in
arXiv, September 2014
DOI 10.1007/978-3-662-44845-8_15
Book ISBNs
978-3-66-244844-1, 978-3-66-244845-8

Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Aikaterini Vrekou, Ioannis Vlahavas, Tsoumakas, Grigorios, Spyromitros-Xioufis, Eleftherios, Vrekou, Aikaterini, Vlahavas, Ioannis

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 24%
Researcher 9 16%
Student > Master 9 16%
Student > Bachelor 4 7%
Professor > Associate Professor 4 7%
Other 11 19%
Unknown 7 12%
Readers by discipline Count As %
Computer Science 32 55%
Engineering 8 14%
Mathematics 2 3%
Business, Management and Accounting 2 3%
Chemical Engineering 1 2%
Other 2 3%
Unknown 11 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 04 March 2015.
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Altmetric has tracked 22,757,090 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 933,974 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 83% of its peers.
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 246,431 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 10,177 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.