Eager learning
Encyclopedia
In artificial intelligence
, 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 lazy learning
, 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 artificial neural network
, is that the target function will be approximated globally during training, thus requiring much less space than a lazy learning system. Eager learning systems also deal much better with noise in the training data. 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.
The main disadvantage with eager learning is that it is generally unable to provide good local approximations in the target function.
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...
, 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 lazy learning
Lazy learning
In artificial intelligence, lazy learning is a learning method in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries.The main advantage gained...
, 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 artificial neural network
Artificial neural network
An artificial neural network , usually called neural network , is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes...
, is that the target function will be approximated globally during training, thus requiring much less space than a lazy learning system. Eager learning systems also deal much better with noise in the training data. Eager learning is an example of offline learning
Offline learning
In machine learning, systems which employ offline learning do not change their approximation of the target function once the initial training phase has been absolved. These systems are also typically examples of eager learning.-See also:...
, 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.
The main disadvantage with eager learning is that it is generally unable to provide good local approximations in the target function.