Offline learning

In machine learning, systems which employ offline learning do not change their approximation of the target function when the initial training phase has been completed.wikipedia
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Eager learning

These systems are also typically examples of eager learning.
Eager learning is an example of offline learning, in which post-training queries to the system have no effect on the system itself, and thus the same query to the system will always produce the same result.

Machine learning

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In machine learning, systems which employ offline learning do not change their approximation of the target function when the initial training phase has been completed.

Learning classifier system

Learning Classifier Systemsclassification schemeclassifier
The major divisions among LCS implementations are as follows: (1) Michigan-style architecture vs. Pittsburgh-style architecture, (2) reinforcement learning vs. supervised learning, (3) incremental learning vs. batch learning, (4) online learning vs. offline learning, (5) strength-based fitness vs. accuracy-based fitness, and (6) complete action mapping vs best action mapping.

Outline of machine learning

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Reverse engineering

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In general, the protocol state-machines can be learned either through a process of offline learning, which passively observes communication and attempts to build the most general state-machine accepting all observed sequences of messages, and online learning, which allows interactive generation of probing sequences of messages and listening to responses to those probing sequences.