This paper is concerned with automatic classification of texts in a medical domain. The process consists in classifying reports of medical discharges into classes defined by the CIE-9-MC codes. We will assign CIE-9-MC codes to reports using either a knn model or support vector machines. One of the added values of this work is the construction of the collection using the discharge reports of a m...

This paper presents an approach to dependency parsing which can utilize any standard machine learning (classification) algorithm. A decision list learner was used in this work. The training data provided in the form of a treebank is converted to a format in which each instance represents information about one word pair, and the classification indicates the existence, direction, and type of the ...

We present a new class of non-linear correlation lters that produce arbitrary quadratic decision surfaces. These new lters rst linearly combine the outputs from other linear or non-linear lters using complex-valued weights. These linear combinations are then passed through a square-law function and again linearly combined to produce these decision surfaces. The linear correlation lters are desi...

This paper focuses on the segmentation and classification of multiple sclerosis lesions in magnetic resonance images. As MRI is the primary tool used in the diagnosis of multiple sclerosis, there is substantial interest in developing an algorithm that will detect lesions from such images. We present a flexible framework in which segmentation and classification are integrated. We assume that we ...

In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score of a linear Support Ve...