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Knowledge Management and Acquisition for Intelligent Systems

Overview of attention for book
Cover of 'Knowledge Management and Acquisition for Intelligent Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection
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    Chapter 2 Improving Named Entity Recognition with Commonsense Knowledge Pre-training
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    Chapter 3 Neurofeedback and AI for Analyzing Child Temperament and Attention Levels
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    Chapter 4 Finding Diachronic Objects of Drifting Descriptions by Similar Mentions
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    Chapter 5 A Max-Min Conflict Algorithm for the Stable Marriage Problem
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    Chapter 6 Empirical Evaluation of Deep Learning-Based Travel Time Prediction
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    Chapter 7 Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network
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    Chapter 8 Constructing Dataset Based on Concept Hierarchy for Evaluating Word Vectors Learned from Multisense Words
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    Chapter 9 Adaptive Database’s Performance Tuning Based on Reinforcement Learning
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    Chapter 10 Prior-Knowledge-Embedded LDA with Word2vec – for Detecting Specific Topics in Documents
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    Chapter 11 Comparative Analysis of Intelligent Personal Agent Performance
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    Chapter 12 Toxicity Prediction by Multimodal Deep Learning
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    Chapter 13 Context-Aware Influence Diffusion in Online Social Networks
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    Chapter 14 Network Embedding via Link Strength Adjusted Random Walk
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    Chapter 15 Study on Influencers of Cryptocurrency Follow-Network on GitHub
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    Chapter 16 A Cross-Domain Theory of Mental Models
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Knowledge Management and Acquisition for Intelligent Systems
Published by
Springer International Publishing, November 2019
DOI 10.1007/978-3-030-30639-7
978-3-03-030638-0, 978-3-03-030639-7

Ohara, Kouzou, Bai, Quan

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Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 20%
Student > Bachelor 1 10%
Professor > Associate Professor 1 10%
Student > Master 1 10%
Unknown 5 50%
Readers by discipline Count As %
Computer Science 4 40%
Economics, Econometrics and Finance 1 10%
Unknown 5 50%