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Principles of Data Science

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Cover of 'Principles of Data Science'

Table of Contents

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    Book Overview
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    Chapter 1 Simulation-Based Data Acquisition
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    Chapter 2 Coding of Bits for Entities by Means of Discrete Events (CBEDE): A Method of Compression and Transmission of Data
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    Chapter 3 Big Biomedical Data Engineering
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    Chapter 4 Big Data Preprocessing: An Application on Online Social Networks
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    Chapter 5 Feature Engineering
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    Chapter 6 Data Summarization Using Sampling Algorithms: Data Stream Case Study
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    Chapter 7 Fast Imputation: An Algorithmic Formalism
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    Chapter 8 A Scientific Perspective on Big Data in Earth Observation
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    Chapter 9 Visualizing High-Dimensional Data Using t-Distributed Stochastic Neighbor Embedding Algorithm
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    Chapter 10 Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches
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    Chapter 11 Applications in Financial Industry: Use-Case for Fraud Management
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    Chapter 12 Stochastic Analysis for Short- and Long-Term Forecasting of Latin American Country Risk Indexes
Attention for Chapter 10: Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches
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Chapter title
Active and Machine Learning for Earth Observation Image Analysis with Traditional and Innovative Approaches
Chapter number 10
Book title
Principles of Data Science
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-43981-1_10
Book ISBNs
978-3-03-043980-4, 978-3-03-043981-1
Authors

Corneliu Octavian Dumitru, Gottfried Schwarz, Gabriel Dax, Vlad Andrei, Dongyang Ao, Mihai Datcu

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