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This paper revisits supervised machine learning for multiclass problems with the assumption that all classes cannot be represented in a training set. This is common in many applications in which there are numerous classes or in which some classes are exceedingly rare. In this paper we propose the use of a decision function to serve in place of the decision boundaries which are used in many machine...
We present a spectral embedding technique for semi-supervised pattern classification. Importance scores of features are firstly evaluated with a semi-supervised feature selection algorithm by Zhao et al. Training data are then embedded into a low-dimensional space with a spectral mapping derived from the selected and weighted feature vectors with which test data are classified by the nearest neighbor...
Fast fuzzy support vector machines (FFSVMs) based on the convex hulls are proposed in this paper. Firstly, the convex hull of each class data is generated by using the quick hull algorithm, and the data points lying inside the convex hull are not important to form FSVMs and then discarded. Secondly, the reduced training set consisting of the convex points is used to train the FFSVMs. Thirdly, the...
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