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- W4285220035 abstract "Lately, there has been a shift to multi-task learning. Multi-task learning performs better than the classical single task learning by learning from the training signals inherent in all the tasks. Inspired by multi-task least squares twin support vector machine, we propose a robust multi-task least squares twin support vector machine. In the proposed work, we introduce an error factor which successfully handles the noise. The proposed model is easy to implement and fast. This allows the model to be of direct application to larger and real-world data sets. In addition, the model deals with nonlinear data patterns by using kernel trick." @default.
- W4285220035 created "2022-07-14" @default.
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- W4285220035 date "2022-01-01" @default.
- W4285220035 modified "2023-10-01" @default.
- W4285220035 title "Robust Multi-task Least Squares Twin Support Vector Machines for Classification" @default.
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- W4285220035 doi "https://doi.org/10.1007/978-981-19-0840-8_29" @default.
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