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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

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    Book Overview
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    Chapter 1 AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling
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    Chapter 2 A Novel Incremental Covariance-Guided One-Class Support Vector Machine
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    Chapter 3 Learning Efficiently in Semantic Based Regularization
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    Chapter 4 Persistent Roles in Online Social Networks
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    Chapter 5 A Split-Merge DP-means Algorithm to Avoid Local Minima
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    Chapter 6 Efficient Distributed Decision Trees for Robust Regression
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    Chapter 7 Fast Hoeffding Drift Detection Method for Evolving Data Streams
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    Chapter 8 Differentially Private User Data Perturbation with Multi-level Privacy Controls
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    Chapter 9 On Dynamic Feature Weighting for Feature Drifting Data Streams
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    Chapter 10 Modeling Sequential Preferences with Dynamic User and Context Factors
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    Chapter 11 Node Re-Ordering as a Means of Anomaly Detection in Time-Evolving Graphs
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    Chapter 12 Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection
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    Chapter 13 Credible Review Detection with Limited Information Using Consistency Features
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    Chapter 14 Interactive Visual Data Exploration with Subjective Feedback
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    Chapter 15 Coupled Hierarchical Dirichlet Process Mixtures for Simultaneous Clustering and Topic Modeling
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    Chapter 16 Enhancing Traffic Congestion Estimation with Social Media by Coupled Hidden Markov Model
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    Chapter 17 Learning Distributed Representations of Users for Source Detection in Online Social Networks
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    Chapter 18 Linear Bandits in Unknown Environments
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    Chapter 19 Ballpark Learning: Estimating Labels from Rough Group Comparisons
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    Chapter 20 An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming
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    Chapter 21 Detecting Public Influence on News Using Topic-Aware Dynamic Granger Test
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    Chapter 22 Exploring a Mixed Representation for Encoding Temporal Coherence
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    Chapter 23 Local Roots: A Tree-Based Subgoal Discovery Method to Accelerate Reinforcement Learning
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    Chapter 24 Copula PC Algorithm for Causal Discovery from Mixed Data
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    Chapter 25 Selecting Collaborative Filtering Algorithms Using Metalearning
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    Chapter 26 A Bayesian Network Model for Interesting Itemsets
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    Chapter 27 Top-N Recommendation via Joint Cross-Domain User Clustering and Similarity Learning
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    Chapter 28 Measuring the Stability of Feature Selection
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    Chapter 29 Aspect Mining with Rating Bias
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    Chapter 30 Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
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    Chapter 31 Subgroup Discovery with Proper Scoring Rules
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    Chapter 32 Consistency of Probabilistic Classifier Trees
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    Chapter 33 Actively Interacting with Experts: A Probabilistic Logic Approach
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    Chapter 34 Augmented Leverage Score Sampling with Bounds
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    Chapter 35 Anti Imitation-Based Policy Learning
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    Chapter 36 Cancer: Another Algorithm for Subtropical Matrix Factorization
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    Chapter 37 Robust Principal Component Analysis by Reverse Iterative Linear Programming
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    Chapter 38 Multilabel Classification on Heterogeneous Graphs with Gaussian Embeddings
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    Chapter 39 M-Flash: Fast Billion-Scale Graph Computation Using a Bimodal Block Processing Model
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    Chapter 40 Improving Locality Sensitive Hashing Based Similarity Search and Estimation for Kernels
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    Chapter 41 Continuous Kernel Learning
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    Chapter 42 Temporal PageRank
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    Chapter 43 Discovering Topically- and Temporally-Coherent Events in Interaction Networks
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    Chapter 44 Proactive Transfer Learning for Heterogeneous Feature and Label Spaces
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    Chapter 45 Asynchronous Distributed Incremental Computation on Evolving Graphs
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    Chapter 46 Infection Hot Spot Mining from Social Media Trajectories
  48. Altmetric Badge
    Chapter 47 Learning to Aggregate Using Uninorms
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    Chapter 48 Sequential Labeling with Online Deep Learning: Exploring Model Initialization
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    Chapter 49 Warped Matrix Factorisation for Multi-view Data Integration
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    Chapter 50 Communication-Efficient Distributed Online Learning with Kernels
Attention for Chapter 47: Learning to Aggregate Using Uninorms
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Chapter title
Learning to Aggregate Using Uninorms
Chapter number 47
Book title
Machine Learning and Knowledge Discovery in Databases
Published in
Lecture notes in computer science, September 2016
DOI 10.1007/978-3-319-46227-1_47
Book ISBNs
978-3-31-946226-4, 978-3-31-946227-1
Authors

Vitalik Melnikov, Eyke Hüllermeier

Editors

Paolo Frasconi, Niels Landwehr, Giuseppe Manco, Jilles Vreeken

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 56%
Other 1 11%
Lecturer > Senior Lecturer 1 11%
Student > Bachelor 1 11%
Student > Master 1 11%
Other 0 0%
Readers by discipline Count As %
Computer Science 6 67%
Biochemistry, Genetics and Molecular Biology 1 11%
Materials Science 1 11%
Engineering 1 11%
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 08 August 2017.
All research outputs
#15,384,302
of 22,888,307 outputs
Outputs from Lecture notes in computer science
#4,648
of 8,127 outputs
Outputs of similar age
#214,446
of 336,136 outputs
Outputs of similar age from Lecture notes in computer science
#266
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Altmetric has tracked 22,888,307 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,127 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 491 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.