The ECIR 2016 paper has been accepted!

The paper “Large-scale Kernel-based Language Learning through the Ensemble Nystrom methods” by Danilo Croce and Roberto Basili has been accepted at the 8th European Conference on Information Retrieval (ECIR 2016) that will be held on 20-23 March 2016 in Padua, Italy (acceptance rate: 21%)

The list of accepted paper can be browsed at this link.

Abstract: Kernel methods have been used by many Machine Learning paradigms, achieving state-of-the-art performances in many Language Learning tasks. One drawback of expressive kernel functions, such as Sequence or Tree kernels, is the time and space complexity required both in learning and classification. In this paper, the Nystrom methodology is studied as a viable solution to face these scalability issues.
By mapping data in low-dimensional spaces as kernel space approximations, the proposed methodology positively impacts on scalability through compact linear representation of highly structured data. Computation can be also distributed on several machines by adopting the so-called Ensemble Nystrom Method.
Experimental results show that an accuracy comparable with state-of-the-art kernel-based methods can be obtained by reducing of orders of magnitude the required operations and enabling the adoption of datasets containing more than one million examples.

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