In this paper we develop a complete methodology for document classification and clustering. We start by investigating how the choice of document features influences the performance of a document classifier and then use our findings to develop a clustering method suitable for document collections. From our study of the effect of frequency transformation, term weighting and dimensionality reducti...

Navigation path optimization derives its cost function from the total cost of travel. These costs can accrue from distance, traffic patterns, preferred order of node sequencing, maximum preferred distance between nodes, and other pragmatic considerations (node-node costs). For a traveling salesman problem (TSP), these costs are usually distances. This paper considers the effects of the initial ...

During recent decades, there have been a great number of research articles studying interior-point methods for solving problems in mathematical programming and constrained optimization. Stewart and O’Leary obtained an upper bound for scaled pseudoinverses sup W∈P ‖(W 12X)+W 2 ‖2 of a matrix X where P is a set of diagonal positive definite matrices. We improved their results to obtain the suprem...

Phytoremediation refers to the use of plants for extraction and detoxification of pollutants, providing a new and powerful weapon against a polluted environment. In some plants, such as Thlaspi spp, heavy metal ATPases are involved in overall metal ion homeostasis and hyperaccumulation. P1B-ATPases pump a wide range of cations, especially heavy metals, across membranes against their electrochem...

So far, boosting has been used to improve the quality of moderately accurate learning algorithms, by weighting and combining many of their weak hypotheses into a final classifier with theoretically high accuracy. In a recent work (Sebban, Nock and Lallich, 2001), we have attempted to adapt boosting properties to data reduction techniques. In this particular context, the objective was not only t...

So far, boosting has been used to improve the quality of moderately accurate learning algorithms, by weighting and combining many of their weak hypotheses into a final classifier with theoretically high accuracy. In a recent work (Sebban, Nock and Lallich, 2001), we have attempted to adapt boosting properties to data reduction techniques. In this particular context, the objective was not only t...

This thesis shows that probabilistic word sense disambiguation systems based on established statistical methods are strong competitors to current state-of-the-art word sense disambiguation (WSD) systems. We begin with a survey of approaches to WSD, and examine their performance in the systems submitted to the Senseval-2 WSD evaluation exercise. We discuss existing resources for WSD, and investi...

Multi-label classification is an extension of classical multi-class one, where any instance can be associated with several classes simultaneously and thus the classes are no longer mutually exclusive. It was experimentally shown that the distance-weighted k-nearest neighbour (DWkNN) algorithm is superior to the original kNN rule for multi-class learning. But, it has not been investigated whethe...

During recent decades, there have been a great number of research articles studying interior-point methods for solving problems in mathematical programming and constrained optimization. Stewart and O'Leary obtained an upper bound for scaled pseudoinverses sup W2P k(W 1 2 X) + W 1 2 k 2 of a matrix X where P is a set of diagonal positive deenite matrices. We improved their results to obtain the ...

To enhance extreme corrupted speech signals, an Improved Psychoacoustically Motivated Spectral Weighting Rule (IPMSWR) is proposed, that controls the predefined residual noise level by a time-frequency dependent parameter. Unlike conventional Psychoacoustically Motivated Spectral Weighting Rules (PMSWR), the level of the residual noise is here varied throughout the enhanced speech based on the ...