Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation ...
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Research on the use of augmented reality technology in museums is mostly limited to scientific knowledge. The use of wearable device technology learning materials to benefit students in the process of English learning...
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The nonlinear effects of partial erasure and transition shift, however, often limit the performance attained by the partial response maximum likelihood (PRML) detector because of model mismatch. Conventionally, the no...
The nonlinear effects of partial erasure and transition shift, however, often limit the performance attained by the partial response maximum likelihood (PRML) detector because of model mismatch. Conventionally, the nonlinear effects are either ignored or approximated by linearization technique. In the article, a PRML detector for the PR4 model including the nonlinear effects has been developed to improve the detector performance. The new representation is more accurate and the corresponding PRML detector has better performance without increasing the realization complexity. Computer simulation results show that the new representation outperforms the conventional ones due to the enhanced modeling capability. The method is also expected to be applied in the high order partial response channel.
Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised a...
ISBN:
(数字)9781728128207
ISBN:
(纸本)9781728128214
Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised and unsupervised real-world problems. However, RL is one of state-of-the-art topic due to the opaque aspects in design and implementation. Also, in which situation we will get performance gain from RL is still unclear. Therefore, This study firstly examines the impact of Experience Replay in Deep Q-Learning agent with Self-Driving Car application. Secondly, The impact of Eligibility Trace in RNN A3C agents with Breakout AI game application is studied. Our results indicated that these two techniques enhance RL performance by more than 20 percent as compared with traditional RL methods.
Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation ...
ISBN:
(数字)9781728128207
ISBN:
(纸本)9781728128214
Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people's preferences usually vary with time; the traditional MF-based methods could not properly capture the change of users' interests. In this paper, by incorporating the recurrent neural network (RNN) into MF, we develop a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. A learning model is proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct the experiments on real world dataset to demonstrate the practicability.
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