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The optimal behavior is learned using support vector regression (SVR).

The inverse kinematic relationship of the manipulator is learned using a Kohonen's self-organizing map.

Our generative model is learned using silhouettes from a set of targets of different classes observed from multiple viewpoints.

The controller behaviour is learned using a reinforcement learning approach, as described in "Learning a control policy".

We refer to this classifier as semi-supervised because it is learned using both labeled and unlabeled data.

Similar to [24], the projection matrix of PCA is learned using 256,000 descriptors randomly sampled from the training set.

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In mammals, fear is learnt using a part of the brain called the amygdala.

One of Skinner's key claims was that with the right practice conditions – meaning that correct behaviour is appropriately rewarded – any task can be learned using simple associations.

The motion experiences are learned using deep learning.

The parameters can be learned using various algorithms.

Grid-based maps are learned using artificial neural networks and naive Bayesian integration.

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