Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
In many applications, modelling techniques are necessary which take into account the inherent variability of given data. In this paper, we present an approach to model class speciï...
A pervasive problem in large relational databases is identity uncertainty which occurs when multiple entries in a database refer to the same underlying entity in the world. Relati...
The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical ap...
This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-o...