The use of kernels forces us to solve SVM optimization problem in the dual space, which involves O(n2) kernel computations. This fact becomes particularly important when very large datasets are used, making SVM learning prohibitively expensive. To overcome this bottleneck, I currently work on the application of cutting plane methods to train classification SVMs with structural kernels, e.g. Tree Kernels, on very large datasets. The approach employs random sampling to obtain considerable speed-ups (over a factor of 10) while delivering the same accuracy when compared to exact SVM solvers.
I am currently a PhD student in Computer Science at the University of Trento, Italy. I received my first degree with honors in Radiophysics and Electronics from Belarusian State University. In the thesis I applied statistical learning algorithms, namely Support Vector Regression, to electric circuit design. Before pursuing my PhD studies I successfully completed the first year of a double degree European Master in Informatics (EuMI) program (offered by the University of Trento and RWTH Aachen University, Germany) with a GPA 28.5 out of 30. Prior to coming to the University of Trento, I worked as an Analytic Expert at ScienceSoft Inc. under the project to develop automatic trading systems for financial markets. I researched and suggested for implementation a number of state-of-the-art machine learning and data mining techniques to enhance the forecasting algorithms. During the summer period in 2009 I completed an internship at Fondazione Bruno Kessler (FBK) working on the Copilosk project under the supervision of Luciano Serafini.
Currently my research is focused on applications of machine learning techniques in computational linguistics, in particular, large-scale cutting plane training of SVMs with structural kernels. My advisor is Alessandro Moschitti.
Develop and implement a framework for efficient large-scale learning of non-linear SVM models with structural kernels.
Aliaksei Severyn, Alessandro Moschitti: Large-Scale Support Vector Learning with Structural Kernels. ECML/PKDD (3) 2010: 229-244
uSVM-TK: integration of SVM-Light-TK and Cutting Plane Algorithm with uniform sampling