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• W2949228149 abstract "Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe a simple and effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data processing tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics." @default.
• W2949228149 created "2019-06-27" @default.
• W2949228149 creator A5044354277 @default.
• W2949228149 date "2007-04-08" @default.
• W2949228149 modified "2023-09-27" @default.
• W2949228149 title "A neural network approach to ordinal regression" @default.
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