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WSC
2001
13 years 10 months ago
Accounting for input model and parameter uncertainty in simulation
Taking into account input-model, input-parameter, and stochastic uncertainties inherent in many simulations, our Bayesian approach to input modeling yields valid point and confide...
Faker Zouaoui, James R. Wilson
NEUROSCIENCE
2001
Springer
14 years 1 months ago
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?
Abstract. Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, a...
Mikel L. Forcada, Rafael C. Carrasco
JMLR
2010
107views more  JMLR 2010»
13 years 3 months ago
Learning Instance-Specific Predictive Models
This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algori...
Shyam Visweswaran, Gregory F. Cooper
FOIS
2006
13 years 10 months ago
Formalizing Ontology Alignment and its Operations with Category Theory
An ontology alignment is the expression of relations between different ontologies. In order to view alignments independently from the language expressing ontologies and from the te...
Antoine Zimmermann, Markus Krötzsch, Jé...
JWSR
2006
168views more  JWSR 2006»
13 years 8 months ago
Facilitating the Specification of Semantic Web Services Using Model-Driven Development
The Semantic Web promises automated invocation, discovery, and composition of Web services by enhancing services with semantic descriptions. An upper ontology for Web services cal...
Gerald C. Gannod, John T. E. Timm, Raynette J. Bro...