3-Computer Science-System Analysis-Learning

learning in systems

Systems can learn {learning, machine}, if parts or part relations can alter. Learning allows new states and/or trajectories. Learning requires mechanisms that can change system relations or rules. Learning requires input information. Separate evaluation function indicates success or failure in performing system function.

competitive learning

Networks {competitive learning} can use units that inhibit nearby units, creating competition among units.

constraint satisfaction

Networks can adjust connection strengths among nodes {constraint satisfaction}|, using feedforward and feedback, to find complete and consistent input-property interpretations.

conspiracy effect

If new pattern is similar to training patterns, new pattern enhances all network nodes {conspiracy effect}. New pattern unrelated to training patterns degrades training.

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Date Modified: 2022.0225