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We propose a new hierarchical generative model for textual data, where words may be generated by topic specific distributions at any level in the hierarchy. This model is naturally well-suited to clustering documents in preset or automatically generated hierarchies, as well as categorising new documents in an existing hierarchy. Training algorithms are derived for both cases, and illustrated on real...
In this paper, we develop a similarity learning version of RELIEF algorithm, called RBS-PSD (for RELIEF-Based Similarity learning) where the learned similarity matrix is projected onto the set of positive, semi-definite matrices. Unfortunately, this algorithm does not perform very well in practice since it does not try to optimize the leave-one-out error or the 0-1 loss. This motivated us to develop...
In this paper, we describe a method to use a graph-based language modeling approach for image retrieval and image categorization. We first mapped image regions to induced concepts and then spatial relationships between these regions to build a graph representation of images. Our method allows to deal with different scenarii, where isolated images or groups of images are used for training and testing...
In this paper, we define an online algorithm to learn the generalized cosine similarity measures for k-NN classification and hence a similarity matrix A corresponding to a bilinear form. In contrary to the standard cosine measure, the normalization is itself dependent on the similarity matrix which makes it impossible to use directly the algorithms developed for learning Mahanalobis distances, based...
In this paper, we propose an algorithm for learning a general class of similarity measures for kNN classification. This class encompasses, among others, the standard cosine measure, as well as the Dice and Jaccard coefficients. The algorithm we propose is an extension of the voted perceptron algorithm and allows one to learn different types of similarity functions (either based on diagonal, symmetric...
We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains.We propose firstly a method for enriching multilingual thesauri which extracts new terms from parallel corpora, and secondly, a new approach for bilingual lexicon...
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