Q3-D3-LSA: D3.js and Generalized Vector Space Models for Statistical Computing-Reference-Cited by-同舟云学术

Q3-D3-LSA: D3.js and Generalized Vector Space Models for Statistical Computing

Published:2018 Issue: Volume: Page:377-424
ISSN:2197-9790
Container-title:Handbook of Big Data Analytics
language:
Short-container-title:

Author:

Borke Lukas,Härdle Wolfgang K.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-18284-1_16

Reference50 articles.

1. Berry M (2003) Survey of text mining: clustering, classification, and retrieval, 1st edn. Springer, New York

2. Borak S, Härdle W, López-Cabrera B (2013) Statistics of financial markets: exercises and solutions, 2nd edn. Springer, Berlin

3. Borke L (2017) Dynamic clustering and visualization of smart data via D3-3D-LSA. Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät. http://dx.doi.org/10.18452/18307

4. Borke L (2017a) TManalyzerQ: provides IR tools in 3 text mining models: BVSM, GVSM(TT) and LSA - QuantNet edition. R package version 0.5.0

5. Borke L (2017b) yamldebugger: YAML parser debugger according to the QuantNet style guide. R package version 1.0

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Understanding Smart Contracts: Hype or Hope?;SSRN Electronic Journal;2021

2. Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies;The European Journal of Finance;2020-07-27

3. Understanding Cryptocurrencies*;Journal of Financial Econometrics;2020

4. A Machine Learning Based Regulatory Risk Index for Cryptocurrencies;SSRN Electronic Journal;2020

5. AN AI APPROACH TO MEASURING FINANCIAL RISK;The Singapore Economic Review;2019-12-30