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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 Incremental Local Evolutionary Outlier Detection for Dynamic Social Networks
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    Chapter 2 How Long Will She Call Me? Distribution, Social Theory and Duration Prediction
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    Chapter 3 Discovering Nested Communities
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    Chapter 4 CSI: Community-Level Social Influence Analysis
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    Chapter 5 Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases
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    Chapter 6 Error Prediction with Partial Feedback
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    Chapter 7 Boot-Strapping Language Identifiers for Short Colloquial Postings
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    Chapter 8 A Pairwise Label Ranking Method with Imprecise Scores and Partial Predictions
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    Chapter 9 Learning Socially Optimal Information Systems from Egoistic Users
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    Chapter 10 Socially Enabled Preference Learning from Implicit Feedback Data
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    Chapter 11 Cross-Domain Recommendation via Cluster-Level Latent Factor Model
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    Chapter 12 Minimal Shrinkage for Noisy Data Recovery Using Schatten- p Norm Objective
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    Chapter 13 Noisy Matrix Completion Using Alternating Minimization
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    Chapter 14 A Nearly Unbiased Matrix Completion Approach
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    Chapter 15 A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank
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    Chapter 16 Efficient Rank-one Residue Approximation Method for Graph Regularized Non-negative Matrix Factorization
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    Chapter 17 Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data
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    Chapter 18 An Analysis of Tensor Models for Learning on Structured Data
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    Chapter 19 Learning Modewise Independent Components from Tensor Data Using Multilinear Mixing Model
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    Chapter 20 Taxonomic Prediction with Tree-Structured Covariances
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    Chapter 21 Position Preserving Multi-Output Prediction
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    Chapter 22 Structured Output Learning with Candidate Labels for Local Parts
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    Chapter 23 Shared Structure Learning for Multiple Tasks with Multiple Views
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    Chapter 24 Using Both Latent and Supervised Shared Topics for Multitask Learning
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    Chapter 25 Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions
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    Chapter 26 Multi-core Structural SVM Training
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    Chapter 27 Multi-label Classification with Output Kernels
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    Chapter 28 Boosting for Unsupervised Domain Adaptation
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    Chapter 29 Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines
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    Chapter 30 A Layered Dirichlet Process for Hierarchical Segmentation of Sequential Grouped Data
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    Chapter 31 A Bayesian Classifier for Learning from Tensorial Data
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    Chapter 32 Prediction with Model-Based Neutrality
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    Chapter 33 Decision-Theoretic Sparsification for Gaussian Process Preference Learning
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    Chapter 34 Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures
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    Chapter 35 Sparsity in Bayesian Blind Source Separation and Deconvolution
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    Chapter 36 Nested Hierarchical Dirichlet Process for Nonparametric Entity-Topic Analysis
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    Chapter 37 Knowledge Intensive Learning: Combining Qualitative Constraints with Causal Independence for Parameter Learning in Probabilistic Models
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    Chapter 38 Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
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    Chapter 39 Machine Learning and Knowledge Discovery in Databases
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    Chapter 40 From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering
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    Chapter 41 Hub Co-occurrence Modeling for Robust High-Dimensional k NN Classification
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    Chapter 42 Fast k NN Graph Construction with Locality Sensitive Hashing
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    Chapter 43 Mixtures of Large Margin Nearest Neighbor Classifiers
Overall attention for this book and its chapters
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
6 tweeters

Readers on

mendeley
1 Mendeley
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Title
Machine Learning and Knowledge Discovery in Databases
Published by
Lecture notes in computer science, January 2013
DOI 10.1007/978-3-642-40991-2
ISBNs
978-3-64-240990-5, 978-3-64-240991-2
Authors

Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Železný

Editors

Blockeel, Hendrik, Kersting, Kristian, Nijssen, Siegfried, Železný, Filip

Twitter Demographics

The data shown below were collected from the profiles of 6 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 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 February 2019.
All research outputs
#6,858,958
of 13,331,643 outputs
Outputs from Lecture notes in computer science
#3,121
of 7,365 outputs
Outputs of similar age
#98,979
of 262,364 outputs
Outputs of similar age from Lecture notes in computer science
#29
of 78 outputs
Altmetric has tracked 13,331,643 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,365 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 57% 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 262,364 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 61% of its contemporaries.
We're also able to compare this research output to 78 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 62% of its contemporaries.