Learning SVM in Kreĭn Spaces.

This paper presents a theoretical foundation for an SVM solver in Kreĭn spaces. Up to now, all methods are based either on the matrix correction, or on non-convex minimization, or on feature-space embedding. Here we justify and evaluate a solution that uses the original (indefinite) similarity measure, in the original Kreĭn space.

Metadata element set in a quality-controlled subject gateway: a step to a health semantic Web.

Background: Quality-controlled subject gateways are Internet services which apply a selected set of targeted measures to support systematic resource discovery. Considerable manual effort is used to process a selection of resources which meet quality criteria and to display a extensive description and indexing of these resources with standards-based metadata.

Objective: Several metadata element sets are proposed to describe, index and quality health resources to be included in a French quality-controlled health gateway called CISMeF.