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The designed ensemble classifier schema is based on the notion of combining knowledge-based and statistical machine learning classification methods aiming to benefit from their merits and minimize their drawbacks.
In other words schema is based on aesthetic.
This organization schema is based on a hierarchy and can be updated incrementally as soon as new cases are available.
The schema is based on the structured modeling paradigm as a formalism for conceiving, representing and manipulating a wide variety of models.
FACT's data schema is based on SysML, providing a unique use case implementation of the benefits of the SysML authoring tool described.
The ensemble schema is based on three classifiers; two are statistical (a Naïve Bayes and a Maximum Entropy learner) and the third one is a knowledge-based tool performing deep analysis of the natural language sentences.
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Moreover, the indices in an access schema are based on labels of neighboring nodes, which are quite different from prior indexing structures.
The XML schemas are based on international standards such as ISO 21127 [19].
The coding categories for 'person schemas' were based on the theoretical framework proposed by Klimoski and Donahue (2001), describing five common types of inference processes in judgment tasks: inferences regarding knowledge, traits, dispositions (probable patterns of behaviour), intentions (immediate goals) and social category membership.
In the schema level, data partitioning is based on the partitioning key.
Second, the data model schema in Social BIMCloud is based on but not limited to the open BIM standard, IFC.
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