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Towards Integrative Machine Learning and Knowledge Extraction

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Cover of 'Towards Integrative Machine Learning and Knowledge Extraction'

Table of Contents

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    Book Overview
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    Chapter 1 Towards Integrative Machine Learning and Knowledge Extraction
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    Chapter 2 Machine Learning and Knowledge Extraction in Digital Pathology Needs an Integrative Approach
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    Chapter 3 Comparison of Public-Domain Software and Services For Probabilistic Record Linkage and Address Standardization
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    Chapter 4 Better Interpretable Models for Proteomics Data Analysis Using Rule-Based Mining
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    Chapter 5 Probabilistic Logic Programming in Action
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    Chapter 6 Persistent Topology for Natural Data Analysis — A Survey
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    Chapter 7 Predictive Models for Differentiation Between Normal and Abnormal EEG Through Cross-Correlation and Machine Learning Techniques
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    Chapter 8 A Brief Philosophical Note on Information
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    Chapter 9 Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
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    Chapter 10 A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images
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    Chapter 11 Topological Characteristics of Oil and Gas Reservoirs and Their Applications
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    Chapter 12 Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment
Attention for Chapter 8: A Brief Philosophical Note on Information
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Chapter title
A Brief Philosophical Note on Information
Chapter number 8
Book title
Towards Integrative Machine Learning and Knowledge Extraction
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-69775-8_8
Book ISBNs
978-3-31-969774-1, 978-3-31-969775-8
Authors

Vincenzo Manca

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