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ECML PKDD 2018 Workshops

Overview of attention for book
Cover of 'ECML PKDD 2018 Workshops'

Table of Contents

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    Book Overview
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    Chapter 1 A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting
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    Chapter 2 Calibrating the Mean-Reversion Parameter in the Hull-White Model Using Neural Networks
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    Chapter 3 Deep Factor Model
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    Chapter 4 A Comparison of Neural Network Methods for Accurate Sentiment Analysis of Stock Market Tweets
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    Chapter 5 A Progressive Resampling Algorithm for Finding Very Sparse Investment Portfolios
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    Chapter 6 ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations
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    Chapter 7 Testing for Self-excitation in Financial Events: A Bayesian Approach
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    Chapter 8 A Web Crawling Environment to Support Financial Strategies and Trend Correlation
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    Chapter 9 A Differential Privacy Workflow for Inference of Parameters in the Rasch Model
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    Chapter 10 Privacy Preserving Client/Vertical-Servers Classification
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    Chapter 11 Privacy Risk for Individual Basket Patterns
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    Chapter 12 Exploring Students Eating Habits Through Individual Profiling and Clustering Analysis
Attention for Chapter 10: Privacy Preserving Client/Vertical-Servers Classification
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Readers on

mendeley
5 Mendeley
Summary
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Chapter title
Privacy Preserving Client/Vertical-Servers Classification
Chapter number 10
Book title
ECML PKDD 2018 Workshops
Published by
Springer, Cham, September 2018
DOI 10.1007/978-3-030-13463-1_10
Book ISBNs
978-3-03-013462-4, 978-3-03-013463-1
Authors

Derian Boer, Zahra Ahmadi, Stefan Kramer, Boer, Derian, Ahmadi, Zahra, Kramer, Stefan

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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 2 40%
Student > Ph. D. Student 1 20%
Professor 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 3 60%
Engineering 1 20%
Unknown 1 20%

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