This paper describes a framework for defining domain specific Feature Functions in a user friendly form to be used in a Maximum Entropy Markov Model (MEMM) for the Named Entity Re...
– Probabilistic Inference Networks are becoming increasingly popular for modeling and reasoning in uncertain domains. In the past few years, many efforts have been made in learni...
We present a Semantic Optimized Service Discovery (SemOSD) approach capable of handling Web service search requests on a fine-grained level of detail where we augment semantic ser...
In this paper we present a general hybrid systems modeling framework to describe the flow of traffic in communication networks. To characterize network behavior, these models use...
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem b...