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Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d. assumption, one novel framework called Field Support Vector Machine (F-SVM) is proposed...
We consider the problem of semi-supervised learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better utilize the information from unlabeled data by formulating them as a three-class (-1,+1, 0) mixture, where class 0 represents the irrelevant data. This distinguishes our work from the traditional SSL problem where unlabeled...
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