Using Contextual Multi-Armed Bandit Algorithms for Recommending Investment in Stock Market
碩士 === 國立中山大學 === 資訊管理學系研究所 === 104 === The Contextual Bandit Problem (CMAB) is usually used to recommend for online applications on article, music, movie, etc. One leading algorithm for contextual bandit is the LinUCB algorithm, which updates internal linear regression models by the partial feedbac...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2016
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/n3qyn2 |