Norbert Roma

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Support vector machine (SVM) learning algorithms focus on finding the hyperplane that maximizes the margin (the distance from the separating hyperplane to the nearest examples) since this criterion provides a good upper bound of the generalization error. When applied to text classification, these learning algorithms lead to SVMs with excellent precision but(More)
This paper proposes a new, scalable and efficient VLSI architecture for real-time sub-pixel motion estimation. The proposed structure is optimized for search strategies using small search ranges, such as hierarchical or sub-pel refinement algorithms. Based on the proposed architecture, a highly modular and configurable motion estimation co-processor capable(More)
In this paper, we describe the system and methods used for the CLARITECH entries in the TREC-8 Filtering Track. Our focus of participation was on the adaptive filtering task, as this comes closest to actual applications. In TREC-7, we proposed, evaluated, and proved effective two algorithms for threshold setting and updating—the delivery ratio mechanism,(More)
The Clairvoyance team participated in the Filtering Track, submitting the maximum number of runs in each of the filtering categories: Adaptive, Batch, and Routing. We had two distinct goals this year: (1) to establish the generalizability of our approach to adaptive filtering and (2) to experiment with relatively more "radical" approaches to batch filtering(More)
The Clairvoyance team participated in the Filtering Track, submitting two runs in the Batch Filtering category. While we have been exploring the question of both topic modeling and ensemble filter construction (as in our previous TREC filtering experiments [5]), we had one distinct objective this year, to explore the viability of monolithic filters in(More)
A new adaptive motion estimation algorithm is proposed in this paper. When compared with other fast search approaches, such as the H.264/AVC oriented EPZS algorithm, this algorithm significantly speeds up the motion estimation procedure and substantially decreases the memory requirements. Moreover, it also makes use of significantly fewer memory accesses,(More)
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