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UML 2 Toolkit
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WSC '05 Proceedings of the 37th conference on Winter simulation
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
An integrated approach to high-level information fusion
Information Fusion
Multisensor Data Fusion
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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Intelligence applications exploit heterogeneous data using High-Level fusion systems to gain information superiority. Whereas Low-Level fusion systems have well established frameworks, High-Level fusion has not yet achieved the same level of maturity. Most High-Level systems implement specialized algorithms that yield useful results, albeit for a very narrow input space, and are characterized by stove-pipe architectures and a fragmented workflow. Recombinant Cognition Synthesis bridges the implementation gap of existing fusion models by defining a comprehensive framework of semantic, temporal, and geospatial enablers comprising the primitives, functions, and models, which through a recombinant workflow, maximize the data exploitation value-chain. This paper presents a methodology and the underlying architectural components necessary to implement a unified High-Level fusion intelligence application, followed by a case study that demonstrates the resulting improvements in knowledge discovery and predictive accuracy.