Zobrazeno 1 - 10
of 150
pro vyhledávání: '"Ebadzadeh, Mohammad Mehdi"'
Applying machine learning methods to forecast stock prices has been one of the research topics of interest in recent years. Almost few studies have been reported based on generative adversarial networks (GANs) in this area, but their results are prom
Externí odkaz:
http://arxiv.org/abs/2301.05693
Financial time series prediction, a growing research topic, has attracted considerable interest from scholars, and several approaches have been developed. Among them, decomposition-based methods have achieved promising results. Most decomposition-bas
Externí odkaz:
http://arxiv.org/abs/2212.14687
Trust Region Policy Optimization (TRPO) is a popular and empirically successful policy search algorithm in reinforcement learning (RL). It iteratively solved the surrogate problem which restricts consecutive policies to be close to each other. TRPO i
Externí odkaz:
http://arxiv.org/abs/2110.13373
Publikováno v:
In Expert Systems With Applications 15 June 2024 244
Inspired by Double Q-learning algorithm, the Double-DQN (DDQN) algorithm was originally proposed in order to address the overestimation issue in the original DQN algorithm. The DDQN has successfully shown both theoretically and empirically the import
Externí odkaz:
http://arxiv.org/abs/2108.04115
In recent years, there have been many deep structures for Reinforcement Learning, mainly for value function estimation and representations. These methods achieved great success in Atari 2600 domain. In this paper, we propose an improved architecture
Externí odkaz:
http://arxiv.org/abs/2107.14457
The K-means algorithm is among the most commonly used data clustering methods. However, the regular K-means can only be applied in the input space and it is applicable when clusters are linearly separable. The kernel K-means, which extends K-means in
Externí odkaz:
http://arxiv.org/abs/2012.02805
Publikováno v:
Neurocomputing, vol. 470, pp. 139-153, 2022
In this paper, a novel stepwise learning approach based on estimating desired premise parts' outputs by solving a constrained optimization problem is proposed. This learning approach does not require backpropagating the output error to learn the prem
Externí odkaz:
http://arxiv.org/abs/2012.01935
Publikováno v:
In Applied Soft Computing November 2023 148
Autor:
Abdallah, Mohammed, Mohammadi, Babak, Nasiri, Hamid, Katipoğlu, Okan Mert, Abdalla, Modawy Adam Ali, Ebadzadeh, Mohammad Mehdi
Publikováno v:
In Energy Reports November 2023 10:4198-4217