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Multi-class supervised learning systems require the knowledge of the entire range of labels they predict. Often when learnt incrementally, they suffer from catastrophic forgetting. To avoid this, generous leeways have to be made to the philosophy of incremental learning that either forces a part of the machine to not learn, or to retrain the machine again with a selection of the historic data. ...
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ژورنال
عنوان ژورنال: Philosophy and Phenomenological Research
سال: 2001
ISSN: 0031-8205,1933-1592
DOI: 10.1111/j.1933-1592.2001.tb00073.x