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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 Theory-Practice Interplay in Machine Learning – Emerging Theoretical Challenges
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    Chapter 2 Are We There Yet?
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    Chapter 3 The Growing Semantic Web
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    Chapter 4 Privacy in Web Search Query Log Mining
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    Chapter 5 Highly Multilingual News Analysis Applications
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    Chapter 6 Combining Instance-Based Learning and Logistic Regression for Multilabel Classification
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    Chapter 7 On Structured Output Training: Hard Cases and an Efficient Alternative
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    Chapter 8 Sparse Kernel SVMs via Cutting-Plane Training
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    Chapter 9 Hybrid Least-Squares Algorithms for Approximate Policy Evaluation
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    Chapter 10 A Self-training Approach to Cost Sensitive Uncertainty Sampling
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    Chapter 11 Learning Multi-linear Representations of Distributions for Efficient Inference
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    Chapter 12 Cost-Sensitive Learning Based on Bregman Divergences
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    Chapter 13 RTG: A Recursive Realistic Graph Generator Using Random Typing
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    Chapter 14 Taxonomy-Driven Lumping for Sequence Mining
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    Chapter 15 On Subgroup Discovery in Numerical Domains
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    Chapter 16 Harnessing the Strengths of Anytime Algorithms for Constant Data Streams
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    Chapter 17 Identifying the Components
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    Chapter 18 Two-Way Analysis of High-Dimensional Collinear Data
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    Chapter 19 A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process
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    Chapter 20 Evaluation Measures for Multi-class Subgroup Discovery
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    Chapter 21 Empirical Study of Relational Learning Algorithms in the Phase Transition Framework
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    Chapter 22 Topic Significance Ranking of LDA Generative Models
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    Chapter 23 Communication-Efficient Classification in P2P Networks
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    Chapter 24 A Generalization of Forward-Backward Algorithm
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    Chapter 25 Mining Graph Evolution Rules
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    Chapter 26 Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
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    Chapter 27 Adaptive XML Tree Classification on Evolving Data Streams
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    Chapter 28 A Condensed Representation of Itemsets for Analyzing Their Evolution over Time
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    Chapter 29 Non-redundant Subgroup Discovery Using a Closure System
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    Chapter 30 PLSI: The True Fisher Kernel and beyond
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    Chapter 31 Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization
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    Chapter 32 One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs
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    Chapter 33 Conference Mining via Generalized Topic Modeling
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    Chapter 34 Within-Network Classification Using Local Structure Similarity
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    Chapter 35 Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC)
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    Chapter 36 Kernel Polytope Faces Pursuit
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    Chapter 37 Soft Margin Trees
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    Chapter 38 Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs
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    Chapter 39 Margin and Radius Based Multiple Kernel Learning
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    Chapter 40 Inference and Validation of Networks
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    Chapter 41 Binary Decomposition Methods for Multipartite Ranking
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    Chapter 42 Leveraging Higher Order Dependencies between Features for Text Classification
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    Chapter 43 Syntactic Structural Kernels for Natural Language Interfaces to Databases
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    Chapter 44 Active and Semi-supervised Data Domain Description
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    Chapter 45 A Matrix Factorization Approach for Integrating Multiple Data Views
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    Chapter 46 Transductive Classification via Dual Regularization
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    Chapter 47 Stable and Accurate Feature Selection
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    Chapter 48 Efficient Sample Reuse in EM-Based Policy Search
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    Chapter 49 Applying Electromagnetic Field Theory Concepts to Clustering with Constraints
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    Chapter 50 An ℓ 1 Regularization Framework for Optimal Rule Combination
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    Chapter 51 A Generic Approach to Topic Models
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    Chapter 52 Feature Selection by Transfer Learning with Linear Regularized Models
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    Chapter 53 Integrating Logical Reasoning and Probabilistic Chain Graphs
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    Chapter 54 Max-Margin Weight Learning for Markov Logic Networks
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    Chapter 55 Parameter-Free Hierarchical Co-clustering by n -Ary Splits
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    Chapter 56 Mining Peculiar Compositions of Frequent Substrings from Sparse Text Data Using Background Texts
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    Chapter 57 Minimum Free Energy Principle for Constraint-Based Learning Bayesian Networks
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    Chapter 58 Kernel-Based Copula Processes
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    Chapter 59 Compositional Models for Reinforcement Learning
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    Chapter 60 Feature Selection for Value Function Approximation Using Bayesian Model Selection
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    Chapter 61 Learning Preferences with Hidden Common Cause Relations
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    Chapter 62 Feature Selection for Density Level-Sets
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    Chapter 63 Efficient Multi-start Strategies for Local Search Algorithms
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    Chapter 64 Considering Unseen States as Impossible in Factored Reinforcement Learning
  66. Altmetric Badge
    Chapter 65 Relevance Grounding for Planning in Relational Domains
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
2 X users
patent
4 patents

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
5 Mendeley
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Title
Machine Learning and Knowledge Discovery in Databases
Published by
ADS, January 2009
DOI 10.1007/978-3-642-04180-8
ISBNs
978-3-64-204179-2, 978-3-64-204180-8
Editors

Wray Buntine, Marko Grobelnik, Dunja Mladenić, John Shawe-Taylor

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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 60%
Student > Ph. D. Student 2 40%
Readers by discipline Count As %
Computer Science 4 80%
Engineering 1 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 23 May 2023.
All research outputs
#6,852,989
of 24,835,287 outputs
Outputs from ADS
#7,777
of 37,265 outputs
Outputs of similar age
#44,034
of 183,623 outputs
Outputs of similar age from ADS
#247
of 909 outputs
Altmetric has tracked 24,835,287 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 37,265 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 78% 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 183,623 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 909 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 72% of its contemporaries.