In machine learning theory, problem classes are distinguished because of di erences in complexity. In 6 , a stochastic model of learning from examples was introduced. This PAClear...
The aim of this paper is to find the finest `observable' and `implementable' equivalence on concurrent processes. This is a part of a larger programme to develop a theor...
Abstract. The classical perceptron algorithm is an elementary algorithm for solving a homogeneous linear inequality system Ax > 0, with many important applications in learning t...
Alexandre Belloni, Robert M. Freund, Santosh Vempa...
A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data (`gene selection'). Numerous gene selecti...
In this paper we extend earlier work on deontic deadlines in CTL to the framework of alternating time temporal logic (ATL). The resulting setting enables us to model several concep...