The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions data points. Recently, multilevel approaches to train SVMs have been developed allow for time-efficient datasets. While regular perform the entire in one—time-consuming—optimization step, first build a hierarchy problems decreasing size that resemble original problem and then an SVM model eac...

Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set possible classes that might appear during operational testing. In such cases, need to think robust classification methods able deal "unknown" properly reject samples belonging never seen training. Notwithstanding, existing classifiers date were mostly developed for closed...