On Generalizable Low False-Positive Learning Using Asymmetric Support Vector Machines
نویسندگان
چکیده
منابع مشابه
Low false positive learning with support vector machines
Most machine learning systems for binary classification are trained using algorithms that maximize the accuracy and assume that false positives and false negatives are equally bad. However, in many applications, these two types of errors may have very different costs. For instance, in medical screening applications, falsely determining that a patient is healthy is much more serious than falsely...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2013
ISSN: 1041-4347
DOI: 10.1109/tkde.2012.46