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Similarity-Based Pattern Recognition

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Cover of 'Similarity-Based Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 A Novel Data Representation Based on Dissimilarity Increments
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    Chapter 2 Characterizing Multiple Instance Datasets
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    Chapter 3 Supervised Learning of Diffusion Distance to Improve Histogram Matching
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    Chapter 4 Similarity Analysis from Limiting Quantum Walks
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    Chapter 5 Introducing Negative Evidence in Ensemble Clustering Application in Automatic ECG Analysis
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    Chapter 6 Dissimilarity Representations for Low-Resolution Face Recognition
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    Chapter 7 Deep Metric Learning Using Triplet Network
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    Chapter 8 Cluster Merging Based on Dominant Sets
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    Chapter 9 An Adaptive Radial Basis Function Kernel for Support Vector Data Description
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    Chapter 10 Robust Initialization for Learning Latent Dirichlet Allocation
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    Chapter 11 Unsupervised Motion Segmentation Using Metric Embedding of Features
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    Chapter 12 Transitive Assignment Kernels for Structural Classification
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    Chapter 13 Large Scale Indefinite Kernel Fisher Discriminant
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    Chapter 14 Similarity-Based User Identification Across Social Networks
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    Chapter 15 Dominant-Set Clustering Using Multiple Affinity Matrices
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    Chapter 16 Discovery of Salient Low-Dimensional Dynamical Structure in Neuronal Population Activity Using Hopfield Networks
Attention for Chapter 14: Similarity-Based User Identification Across Social Networks
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Citations

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Chapter title
Similarity-Based User Identification Across Social Networks
Chapter number 14
Book title
Similarity-Based Pattern Recognition
Published by
Springer, Cham, October 2015
DOI 10.1007/978-3-319-24261-3_14
Book ISBNs
978-3-31-924260-6, 978-3-31-924261-3
Authors

Katerina Zamani, Georgios Paliouras, Dimitrios Vogiatzis, Zamani, Katerina, Paliouras, Georgios, Vogiatzis, Dimitrios

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 50%
Student > Bachelor 1 25%
Student > Ph. D. Student 1 25%
Readers by discipline Count As %
Computer Science 3 75%
Economics, Econometrics and Finance 1 25%

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