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- W1510364837 abstract "In recent years, there has been growing interest in applying techniques that incorporate, knowledge from unlabeled data into systems performing supervised learning. The main motivation for this is the belief that classification performance can be improved by utilizing the ccntextual information provided by unlabeled data. Most of the approaches that have been proposed for this problem are generative; that is, they are based on directly modelling the class distribution of the training examples, This paper approaches the problem from a discriminant classifier perspective, and proposes a new technique by which feedforward neural networks can be trained from a dataset consisting of both labeled and unlabeled data. Results are presented from applying the technique to several datasets from the UCI repository. The results show that on some datasets the use of unlabeled examples can lead to an improvement in classification performance over that of conventional supervised learning. The technique adds little computational overhead to the standard supervised technique." @default.
- W1510364837 created "2016-06-24" @default.
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- W1510364837 date "2003-01-01" @default.
- W1510364837 modified "2023-09-26" @default.
- W1510364837 title "A GA-based neural network weight optimization technique for semi-supervised classifier learning" @default.
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