We present a Generalized Lotka-Volterra (GLV) based approach for modeling and simulation of supervised inductive learning, and construction of an efficient classification algorith...
Karen Hovsepian, Peter Anselmo, Subhasish Mazumdar
Abstract. This paper presents a constraint-based technique for discovering a rich class of inductive invariants (boolean combinations of polynomial inequalities of bounded degree) ...
Conventional work on scientic discovery such as BACON derives empirical law equations from experimental data. In recent years, SDS introducing mathematical admissibility constrain...
We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-...
Tahira Naseem, Harr Chen, Regina Barzilay, Mark Jo...
Abstract: In this paper we present our ideas to apply constraint satisfaction on business processes. We propose a multi-level constraint satisfaction approach to handle t levels of...