Difference between revisions of "Extreme Learning Process"

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Revision as of 11:02, 12 January 2022

Mathematical Semantic Model (Extreme Learning Process)


Abstract Specification
Context Given the power of Hyperlinked data infrastructure, learning processes must be continuously extended to utilize opportunities presented to users in their respective spatial/temporal contexts.
Goal Create a self-aware process to enable learning.
Success Criteria (Liveness)
  1. A highly available data system that can capture and measure the learning process data of relevant users.
  2. A set of learning assessment tools to reflect the conditions and quality of learning.
  3. A collection of intervention methods captured overtime, to document how to improve learning practices.
Concrete Implementation(s)
Outputs Process Inputs
A tensor-based representation of process.
Boundary Conditions of Extreme Learning Process/(Safety Conditions)
See Extreme Learning Process 1.43