Sentence examples for training observation from inspiring English sources

By doing this, the computational complexity no longer depends on the number of training observation vectors, but rather on the total number of states.

Given a sequence of training observation O=[o 1,…,o T ], the parameters of λ could be learned by maximizing the likelihood of the observation sequence O, i.e., Pr(O|λ).

Given a set of training observation sequences O (r), r=1,2,…,R, an empirical loss function on the training data can be defined as L = ∑ r = 1 R ∑ c = 1 C l c ( O ( r ) ; Λ ) I ( O ( r ) ∈ C c ). (16).

Given a set of training observation sequences O (r), r=1,2,…,R, an empirical loss function on the training data, that can approximate the true Bayes risk is defined as: L = ∑ r = 1 R ∑ c = 1 C l c ( O ; Λ ) I ( O ∈ C c ). (44).

This finding suggests that, following non-matching training, observation of hand actions activates motor representations of foot actions.

Preference is given to higher resolutions, but the number of training observations is taken into account.

The learning algorithm has no prior knowledge as to how many training observations will be presented.

We apply agglomerative hierarchical clustering resulting in a large number of clusters, approximately half the number of the training observations.

A single or a chunk of training observations is discarded as soon as the learning procedure for that particular (single or chunk of) observation(s) is completed.

It is a versatile sequential learning algorithm because the training observations are sequentially (one-by-one or chunk-by-chunk with varying or fixed chunk length) presented to the learning algorithm.

Both NB and LDA are ideal methods when the number of training observations is limited.