Eager learning

<p>In <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a>, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to <a href="/facts/Lazy_learning/sMXw0yna">lazy learning</a>, where generalization beyond the training data is delayed until a query is made to the system.
The main advantage gained in employing an eager learning method, such as an <a href="/facts/Artificial_neural_network/6V1jMlkx">artificial neural network</a>, is that the target function will be approximated globally during training, thus requiring much less space than using a lazy learning system. Eager learning systems also deal much better with noise in the <a href="/facts/Training_data/V3mMQb2G">training data</a>. Eager learning is an example of <a href="/facts/Offline_learning/IuGbxmJE">offline learning</a>, 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.
</p><p>The main disadvantage with eager learning is that it is generally unable to provide good local approximations in the target function.
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<table><tbody><tr><td>Eager Learning</td><td>Lazy Learning</td></tr><tr><td>Model is created as soon as possible</td><td>Model creation is waited until the last point possible</td></tr><tr><td>Processing power consumed sooner</td><td>Processing power consumed later</td></tr><tr><td>Model is ready for query at once</td><td>Waits to create a model until the query</td></tr></tbody></table>

Eager learning open-in-new

Eager learning