Mining Data for Financial Applications (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
133
Utgivningsdatum
2020-01-04
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Bordino, Ilaria / Ferretti, Andrea
Illustrationer
27 Illustrations, color; 10 Illustrations, black and white; IX, 133 p. 37 illus., 27 illus. in color
Dimensioner
234 x 156 x 8 mm
Vikt
213 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030377199

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Mining Data for Financial Applications (häftad)

Mining Data for Financial Applications

4th ECML PKDD Workshop, MIDAS 2019, Wurzburg, Germany, September 16, 2019, Revised Selected Papers

av Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Stefano Pascolutti
Häftad Engelska, 2020-01-04
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This book constitutes revised selected papers from the 4th Workshop on Mining Data for Financial Applications, MIDAS 2019, held in conjunction with ECML PKDD 2019, in Wurzburg, Germany, in September 2019. The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 16 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain.
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    This book constitutes revised selected papers from two workshops held at the 18th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, in Dublin, Ireland, in September 2018, namely: MIDAS 2018 - Third Works...

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Innehållsförteckning

MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning.- Curriculum Learning in Deep Neural Networks for Financial Forecasting.- Representation Learning in Graphs for Credit Card Fraud Detection.- Firms Default Prediction with Machine Learning.- Convolutional Neural Networks, Image Recognition and Financial Time Series Forecasting.- Mining Business Relationships from Stocks and News.- Mining Financial Risk Events from News and Assessing their impact on Stocks.- Monitoring the Business Cycle with Fine-grained, Aspect-based Sentiment Extraction from News.- Multi-step Prediction of Financial Asset Return Volatility Using Parsimonious Autoregressive Sequential Model.- Big Data Financial Sentiment Analysis in the European Bond Markets.- A Brand Scoring System for Cryptocurrencies Based on Social Media Data.