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- W2980172942 abstract "How to leverage knowledge from labelled domain (source) to help classify unlabeled domain (target) is a key problem in the machine learning field. Unsupervised domain adaptation (UDA) provides a solution to this problem and has been well developed for two homogeneous domains. However, when the target domain is unlabeled and heterogeneous with the source domain, current UDA models cannot accurately transfer knowledge from a source domain to a target domain. Benefiting from development of neural networks, this paper presents a new neural network, shared fuzzy equivalence relations neural network (SFER-NN), to address the heterogeneous UDA (HeUDA) problem. SFER-NN transfers knowledge across two domains according to shared fuzzy equivalence relations that can simultaneously cluster features of two domains into several categories. Based on the clustered categories, SFER-NN is constructed to minimize the discrepancy between two domains. Compared to previous works, SFER-NN is more capable of minimizing discrepancy between two domains. As a result of this advantage, SFER-NN delivers a better performance than previous studies using two public datasets." @default.
- W2980172942 created "2019-10-18" @default.
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- W2980172942 date "2019-06-01" @default.
- W2980172942 modified "2023-09-26" @default.
- W2980172942 title "A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios" @default.
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- W2980172942 doi "https://doi.org/10.1109/fuzz-ieee.2019.8858889" @default.
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