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Mining Data for Financial Applications

Overview of attention for book
Cover of 'Mining Data for Financial Applications'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning
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    Chapter 2 Curriculum Learning in Deep Neural Networks for Financial Forecasting
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    Chapter 3 Representation Learning in Graphs for Credit Card Fraud Detection
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    Chapter 4 Firms Default Prediction with Machine Learning
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    Chapter 5 Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting
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    Chapter 6 Mining Business Relationships from Stocks and News
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    Chapter 7 Mining Financial Risk Events from News and Assessing their Impact on Stocks
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    Chapter 8 Monitoring the Business Cycle with Fine-Grained, Aspect-Based Sentiment Extraction from News
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    Chapter 9 Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model
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    Chapter 10 Big Data Financial Sentiment Analysis in the European Bond Markets
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    Chapter 11 A Brand Scoring System for Cryptocurrencies Based on Social Media Data
Attention for Chapter 2: Curriculum Learning in Deep Neural Networks for Financial Forecasting
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Readers on

mendeley
34 Mendeley
Summary
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Chapter title
Curriculum Learning in Deep Neural Networks for Financial Forecasting
Chapter number 2
Book title
Mining Data for Financial Applications
Published by
Springer, Cham, September 2019
DOI 10.1007/978-3-030-37720-5_2
Book ISBNs
978-3-03-037719-9, 978-3-03-037720-5
Authors

Allison Koenecke, Amita Gajewar, Koenecke, Allison, Gajewar, Amita

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 26%
Student > Master 4 12%
Researcher 4 12%
Student > Doctoral Student 3 9%
Other 2 6%
Other 5 15%
Unknown 7 21%
Readers by discipline Count As %
Computer Science 11 32%
Engineering 5 15%
Economics, Econometrics and Finance 3 9%
Physics and Astronomy 2 6%
Mathematics 2 6%
Other 4 12%
Unknown 7 21%

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