This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
Using standard nonlinear programming (NLP) theory, we establish formulas for first and second order directional derivatives for optimal value functions of parametric mathematical ...
In this paper, we present a logic-based computational model for movement theory in Government and Binding Theory. For that purpose, we have designed a language called DISLOG. DISL...
We introduce a framework for studying and solving a class of CSP formulations. The framework allows constraints to be expressed as linear and nonlinear equations, then compiles th...
Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and i...