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Machine Learning and Knowledge Discovery in Databases

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Cover of 'Machine Learning and Knowledge Discovery in Databases'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Maximum Margin Separations in Finite Closure Systems
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    Chapter 2 Discovering Outstanding Subgroup Lists for Numeric Targets Using MDL
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    Chapter 3 A Relaxation-Based Approach for Mining Diverse Closed Patterns
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    Chapter 4 OMBA: User-Guided Product Representations for Online Market Basket Analysis
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    Chapter 5 Online Binary Incomplete Multi-view Clustering
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    Chapter 6 Utilizing Structure-Rich Features to Improve Clustering
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    Chapter 7 Simple, Scalable, and Stable Variational Deep Clustering
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    Chapter 8 Gauss Shift: Density Attractor Clustering Faster Than Mean Shift
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    Chapter 9 Privacy-Preserving Decision Trees Training and Prediction
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    Chapter 10 Poisoning Attacks on Algorithmic Fairness
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    Chapter 11 SpecGreedy: Unified Dense Subgraph Detection
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    Chapter 12 Networked Point Process Models Under the Lens of Scrutiny
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    Chapter 13 FB2vec: A Novel Representation Learning Model for Forwarding Behaviors on Online Social Networks
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    Chapter 14 A Framework for Deep Quantification Learning
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    Chapter 15 PROMO for Interpretable Personalized Social Emotion Mining
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    Chapter 16 Progressive Supervision for Node Classification
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    Chapter 17 Modeling Dynamic Heterogeneous Network for Link Prediction Using Hierarchical Attention with Temporal RNN
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    Chapter 18 GIKT: A Graph-Based Interaction Model for Knowledge Tracing
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    Chapter 19 Simple and Effective Graph Autoencoders with One-Hop Linear Models
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    Chapter 20 Sparse Separable Nonnegative Matrix Factorization
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    Chapter 21 Robust Domain Adaptation: Representations, Weights and Inductive Bias
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    Chapter 22 Target to Source Coordinate-Wise Adaptation of Pre-trained Models
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    Chapter 23 Unsupervised Multi-source Domain Adaptation for Regression
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    Chapter 24 Open Set Domain Adaptation Using Optimal Transport
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    Chapter 25 Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search
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    Chapter 26 Modeling Winner-Take-All Competition in Sparse Binary Projections
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    Chapter 27 LOAD: LSH-Based $$\ell _0$$ ℓ 0 -Sampling over Stream Data with Near-Duplicates
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    Chapter 28 Spatio-Temporal Tensor Sketching via Adaptive Sampling
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    Chapter 29 Orthogonal Mixture of Hidden Markov Models
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    Chapter 30 Poisson Graphical Granger Causality by Minimum Message Length
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    Chapter 31 Counterfactual Propagation for Semi-supervised Individual Treatment Effect Estimation
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    Chapter 32 Real-Time Fine-Grained Freeway Traffic State Estimation Under Sparse Observation
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    Chapter 33 Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
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    Chapter 34 RLTS: Robust Learning Time-Series Shapelets
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    Chapter 35 Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model
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    Chapter 36 Predicting Future Classifiers for Evolving Non-linear Decision Boundaries
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    Chapter 37 Parameterless Semi-supervised Anomaly Detection in Univariate Time Series
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    Chapter 38 The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
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    Chapter 39 Incremental Training of a Recurrent Neural Network Exploiting a Multi-scale Dynamic Memory
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    Chapter 40 Flexible Recurrent Neural Networks
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    Chapter 41 Z-Embedding: A Spectral Representation of Event Intervals for Efficient Clustering and Classification
  43. Altmetric Badge
    Chapter 42 Neural Cross-Domain Collaborative Filtering with Shared Entities
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    Chapter 43 NoisyCUR: An Algorithm for Two-Cost Budgeted Matrix Completion
Attention for Chapter 14: A Framework for Deep Quantification Learning
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Chapter title
A Framework for Deep Quantification Learning
Chapter number 14
Book title
Lecture Notes in Computer Science
Published in
Lecture notes in computer science, February 2021
DOI 10.1007/978-3-030-67658-2_14
Book ISBNs
978-3-03-067657-5, 978-3-03-067658-2
Authors

Lei Qi, Mohammed Khaleel, Wallapak Tavanapong, Adisak Sukul, David Peterson, Qi, Lei, Khaleel, Mohammed, Tavanapong, Wallapak, Sukul, Adisak, Peterson, David

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 13%
Student > Ph. D. Student 1 13%
Professor > Associate Professor 1 13%
Student > Bachelor 1 13%
Unknown 4 50%
Readers by discipline Count As %
Unspecified 1 13%
Computer Science 1 13%
Social Sciences 1 13%
Engineering 1 13%
Unknown 4 50%
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 10 March 2021.
All research outputs
#16,036,872
of 23,802,430 outputs
Outputs from Lecture notes in computer science
#4,679
of 8,165 outputs
Outputs of similar age
#261,163
of 420,221 outputs
Outputs of similar age from Lecture notes in computer science
#6
of 11 outputs
Altmetric has tracked 23,802,430 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 8,165 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 11 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.