For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training....
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimiza...
We introduce a new classification algorithm based on the concept of Symmetric Maximized Minimal distance in Subspace (SMMS). Given the training data of authentic samples and impos...
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system mo...
Ashkan Sharifi Shamili, Christian Bauckhage, Tansu...