A Hebbian-inspired, competitive network is presented which learns to predict the typical semantic features of denoting terms in simple and moderately complex sentences. In addition, the network learns to predict the appearance of syntactically key words, such as prepositions and relative pronouns. Importantly , as a by-product of the network's semantic training, a strong form of syntactic syste...

Healthcare is a competitive business in its own right. Global competition in healthcare adds yet another complex dimension to the success of a healthcare organization. Providing state of the art technology along with the manpower and management skills to bridge boundaries and cultures, confronts today’s healthcare organizations with challenges that, while on the surface may appear simple, may a...

The paper develops a model of destination competitiveness that will enable comparison between countries and between tourism sector industries. The model seeks to capture the main elements of competitiveness highlighted in the general literature, while appreciating the special issues involved in exploring the notion of destination competitiveness as emphasized by tourism researchers. Associated ...

We consider the problem of searching on m current rays for a target of unknown location. If no upper bound on the distance to the target is known in advance, then the optimal competitive ratio is 1+2mm=(m 1)m 1 . We show that if an upper bound of D on the distance to the target is known in advance, then the competitive ratio of any search strategy is at least 1+2mm=(m 1)m 1 O(1= log2 D) which i...

Algorithms for the group testing problem when there is no a priori information on the number of defective items are considered. The efficiency criterion used is the competitive ratio, which is the ratio of the number of tests required by an algorithm when there is no a priori information on the number of defective items, to the number of tests required by an optimal algorithm when the number of...

In the script there is a definition for the competitiveness of an algorithm. However, this definition only holds if we want to evaluate an algorithm by means of its costs. Sometimes, we want to compare algorithms regarding their benefit rather than their costs. In this case, we have to be a bit careful with the definition of a c-competitive algorithm. We say that an algorithm Alg is c-competiti...

In this paper a clustering algorithm for sparsely sampled high-dimensional feature spaces is proposed. The algorithm performs clustering by employing a distance measure that compensates for diierently sized clusters. A sequential version of the algorithm is constructed in the form of a frequency sensitive Competitive Learning scheme. Experiments are conducted on an artiicial gaussian data set a...

At the present time, one of the best methods for semiautomatic image segmentation seems to be the approach based on the fuzzy connectedness principle. First, we identify some deficiencies of this approach and propose a way to improve it, through the introduction of competitive learning. Second, we propose a different approach, based on watersheds. We show that the competitive fuzzy connectednes...