In this paper, we propose a machine learning-based method of multi-document summarization integrating sentence extraction with bunsetsu elimination. We employ Support Vector Machines for both of the modules used. To evaluate the effect of bunsetsu elimination, we participated in the multi-document summarization task at TSC-2 by the following two approaches: (1) sentence extraction only, and (2) sentence extraction + bunsetsu elimination. The results of subjective evaluation at TSC-2 show that both approaches are superior to the Lead-based method from the viewpoint of information coverage. In addition, we made extracts from given abstracts to quantitatively examine the effectiveness of bunsetsu elimination. The experimental results showed that our bunsetsu elimination makes summaries more informative. Moreover, we found that extraction based on SVMs trained by short extracts are better than the Lead-based method, but that SVMs trained by long extracts are not.

For suppressing inter symbol interference, the support vector machine mutliuser detector (SVM-MUD) was adopted as a nonlinear method in direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. To solve the problems of the complexity of SVM-MUD model and the number of support vectors, based on recursive least squares support vector machine (RLS-SVM) and Riemannian geometry, a new algorithm for nonlinear multiuser detector is proposed. The algorithm introduces the forgetting factor to get the support vectors at the first training samples, then, uses Riemannian geometry to train the support vectors again and gets less improved support vectors. Simulation results illustrated that the algorithm simplifies SVM-MUD model at the cost of only a little more bit error rate and decreases the computational complexity. At the same time, the algorithm has an excellent effect on suppressing multipath interference.

Support vector machines (SVMs), newly introduced in the 1990s, are promising approach to pattern recognition. They are able to handle linearly nonseparable problems without difficulty, by combining the maximal margin strategy with the kernel method. This paper addresses a novel SVM-based classification scheme of land cover from polarimetric synthetic aperture radar (SAR) data. Polarimetric observations can reveal existing different scattering mechanisms. As the input into SVMs, the polarimetric feature vectors, composed of intensity of each channel, sometimes complex correlation coefficients and textural information, are prepared. Classification experiments with real polarimetric SAR images are satisfactory. Some important properties of SVMs, for example the relation between the number of support vectors and classification accuracy, are also investigated.