Výsledky vyhledávání - "Szu-Hao Huang"

Akademický článek

Adversarial Attacks Against Reinforcement Learning-Based Portfolio Management Strategy

Autor: Yu-Ying Chen, Chiao-Ting Chen, Chuan-Yun Sang, Yao-Chun Yang, Szu-Hao Huang

Publikováno v: IEEE Access, Vol 9, Pp 50667-50685 (2021)

Many researchers have incorporated deep neural networks (DNNs) with reinforcement learning (RL) in automatic trading systems. However, such methods result in complicated algorithmic trading models with several defects, especially when a DNN model is

Externí odkaz: https://doaj.org/article/e8d1c6aab2c84bf8ad6110b9260dc27a

Zobrazit plný text záznamu

Akademický článek

Improving Generalization in Reinforcement Learning–Based Trading by Using a Generative Adversarial Market Model

Autor: Chia-Hsuan Kuo, Chiao-Ting Chen, Sin-Jing Lin, Szu-Hao Huang

Publikováno v: IEEE Access, Vol 9, Pp 50738-50754 (2021)

With the increasing sophistication of artificial intelligence, reinforcement learning (RL) has been widely applied to portfolio management. However, shortcomings remain. Specifically, because the training environment of an RL-based portfolio optimiza

Externí odkaz: https://doaj.org/article/aec019ca186149d9a73c813270695591

Zobrazit plný text záznamu

Akademický článek

Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

Autor: Pei-Ying Wang, Chiao-Ting Chen, Jain-Wun Su, Ting-Yun Wang, Szu-Hao Huang

Publikováno v: IEEE Access, Vol 9, Pp 55244-55259 (2021)

House price prediction is a popular topic, and research teams are increasingly performing related studies by using deep learning or machine learning models. However, because some studies have not considered comprehensive information that affects hous

Externí odkaz: https://doaj.org/article/e5ed99e82dc447959842e7997d45e66f

Zobrazit plný text záznamu

Akademický článek

Interpretable Stock Anomaly Detection Based on Spatio-Temporal Relation Networks With Genetic Algorithm

Autor: Mei-See Cheong, Mei-Chen Wu, Szu-Hao Huang

Publikováno v: IEEE Access, Vol 9, Pp 68302-68319 (2021)

Instability in financial markets represents a considerable risk to investors; examples of instability include a market crash caused by systematic risks and abnormal stock price volatility caused by artificial hype. The early detection of abnormal beh

Externí odkaz: https://doaj.org/article/ad25e18228e84dce8ae5fbaf4b30c412

Zobrazit plný text záznamu

Akademický článek

Novel Deep Reinforcement Algorithm With Adaptive Sampling Strategy for Continuous Portfolio Optimization

Autor: Szu-Hao Huang, Yu-Hsiang Miao, Yi-Ting Hsiao

Publikováno v: IEEE Access, Vol 9, Pp 77371-77385 (2021)

Quantitative trading targets favorable returns by determining patterns in historical data through statistical or mathematical approaches. With advances in artificial intelligence, many studies have indicated that deep reinforcement learning (RL) can

Externí odkaz: https://doaj.org/article/294961b976c94c6f8d279223cf299d87

Zobrazit plný text záznamu

Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation

Autor: Wun-Ting Yang, Chiao-Ting Chen, Chuan-Yun Sang, Szu-Hao Huang

Publikováno v: ACM Transactions on Intelligent Systems and Technology. 14:1-25

The data of recommendation systems typically only contain the purchased item as positive data and other un-purchased items as unlabeled data. To train a good recommendation model, in addition to the known positive information, we also need high-quali

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::db091effa6c35aaf22eb83d33ebc657f
https://doi.org/10.1145/3582562

Zobrazit plný text záznamu

Modeling Cross-session Information with Multi-interest Graph Neural Networks for the Next-item Recommendation

Autor: Ting-Yun Wang, Chiao-Ting Chen, Ju-Chun Huang, Szu-Hao Huang

Publikováno v: ACM Transactions on Knowledge Discovery from Data. 17:1-28

Next-item recommendation involves predicting the next item of interest of a given user from their past behavior. Users tend to browse and purchase various items on e-commerce websites according to their varied interests and needs, as reflected in the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8fb5094f1484b4be7c59b127399876c6
https://doi.org/10.1145/3532192

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání