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Future Data and Security Engineering

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Cover of 'Future Data and Security Engineering'

Table of Contents

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    Book Overview
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    Chapter 1 Random Local SVMs for Classifying Large Datasets
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    Chapter 2 An Efficient Document Indexing-Based Similarity Search in Large Datasets
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    Chapter 3 Using Local Rules in Random Forests of Decision Trees
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    Chapter 4 A Term Weighting Scheme Approach for Vietnamese Text Classification
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    Chapter 5 Fault Data Analytics Using Decision Tree for Fault Detection
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    Chapter 6 Evaluation of Reliability and Security of the Address Resolution Protocol
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    Chapter 7 Establishing a Decision Tool for Business Process Crowdsourcing
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    Chapter 8 Finding Similar Artists from the Web of Data: A PageRank Based Semantic Similarity Metric
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    Chapter 9 Opinion Analysis in Social Networks Using Antonym Concepts on Graphs
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    Chapter 10 Traffic Speed Data Investigation with Hierarchical Modeling
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    Chapter 11 An Effective Approach to Background Traffic Detection
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    Chapter 12 An Approach for Developing Intelligent Systems in Smart Home Environment
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    Chapter 13 Modelling Sensible Business Processes
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    Chapter 14 Contractual Proximity of Business Services
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    Chapter 15 Energy-Efficient VM Scheduling in IaaS Clouds
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    Chapter 16 Multi-diagram Representation of Enterprise Architecture: Information Visualization Meets Enterprise Information Management
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    Chapter 17 Enhancing the Quality of Medical Image Database Based on Kernels in Bandelet Domain
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    Chapter 18 Information Systems Success: A Literature Review
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    Chapter 19 Facilitating the Design/Evaluation Process of Web-Based Geographic Applications: A Case Study with WINDMash
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    Chapter 20 A Context-Aware Recommendation Framework in E-Learning Environment
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    Chapter 21 Automatic Evaluation of the Computing Domain Ontology
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    Chapter 22 Comics Instance Search with Bag of Visual Words
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    Chapter 23 Defining Membership Functions in Fuzzy Object-Oriented Database Model
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    Chapter 24 Erratum to: Facilitating the Design/Evaluation Process of Web-Based Geographic Applications: A Case Study with WINDMash
Attention for Chapter 1: Random Local SVMs for Classifying Large Datasets
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Citations

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Chapter title
Random Local SVMs for Classifying Large Datasets
Chapter number 1
Book title
Future Data and Security Engineering
Published in
Lecture notes in computer science, November 2015
DOI 10.1007/978-3-319-26135-5_1
Book ISBNs
978-3-31-926134-8, 978-3-31-926135-5
Authors

Thanh-Nghi Do, François Poulet

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 27%
Student > Ph. D. Student 2 18%
Other 1 9%
Student > Bachelor 1 9%
Student > Master 1 9%
Other 1 9%
Unknown 2 18%
Readers by discipline Count As %
Computer Science 6 55%
Agricultural and Biological Sciences 1 9%
Decision Sciences 1 9%
Unknown 3 27%