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- W4313132685 abstract "As an important indicator of economic development, house price prediction is a hot issue in the field of business and research in urban computing. However, due to a variety of influencing factors and sparse transaction records, how to construct a housing price prediction framework is still a challenging problem. However, the existing machine learning and deep learning methods lack consideration of multi-source factors. Therefore, this paper uses the method based on channel exchange network to solve the problem of urban house price prediction. This paper also considers the space-time factors, regional factors and housing price expectations. In this paper, a neural network framework based on channel switching network is designed to capture these factors, and the characteristics of different model training are fused through channel switching" @default.
- W4313132685 created "2023-01-06" @default.
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- W4313132685 date "2022-08-01" @default.
- W4313132685 modified "2023-09-27" @default.
- W4313132685 title "Channel exchange network model for house price prediction" @default.
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- W4313132685 doi "https://doi.org/10.1109/mlise57402.2022.00027" @default.
- W4313132685 hasPublicationYear "2022" @default.
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