Cantitate/Preț
Produs
Total produse
Vezi coșul
Mining Data for Financial Applications de Valerio Bitetta

Mining Data for Financial Applications: Lecture Notes in Computer Science, cartea 11985

Editat de Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Stefano Pascolutti, Giovanni Ponti
en Limba Engleză Paperback – 4 ian 2020
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 Würzburg, 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.

Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 31416 lei

Preț vechi: 39271 lei
-20%

Puncte Express: 471

Carte tipărită la comandă

Livrare economică 10-24 august

Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit de la 40000 lei Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9783030377199
ISBN-10: 3030377199
Pagini: 144
Ilustrații: IX, 133 p. 37 illus., 27 illus. in color.
Dimensiuni: 155 x 235 x 9 mm
Greutate: 0.23 kg
Ediția:1st ed. 2020
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.