Data Regularization
نویسنده
چکیده
Abstract: Quite often real-world data set contain errors and inaccuracies. Most classification models are trained using crisp, sharply classified (black and white) examples only. In many real world problems the soft class labels (shades of gray) are quite natural. In this paper data regularization method has been presented. The method may help to strengthen the confidence in a given data set. Further data processing (learning) may become more stable and may lead to more reliable results.
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