Training Time Complexity

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A Training Time Complexity is a Computational Time Complexity that corresponds to the time interval that takes for an algorithm to learn a model from training data.



References

2019

2017

2008

kNN with preprocessing of training set
training $\Theta(\vert\mathbb{D}\vert L_{ave})$
testing $\Theta( L_{a} + \vert \mathbb{D} \vert M_{ave} M_{a})= \Theta(\vert\mathbb{D}\vert M_{ave} M_{a})$
kNN without preprocessing of training set
training $\Theta(1)$
testing $\Theta( L_{a} + \vert \mathbb{D} \vert L_{ave} M_{a}) = \Theta(\vert\mathbb{D}\vert L_{ave} M_{a})$
Table 14.3: Training and test times for kNN classification.$ M_{ave}$ is the average size of the vocabulary of documents in the collection.