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Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN).
However, in many general and non-vision tasks, neural networks are surpassed by methods such as support vector machines and random forests that are also easier to use and faster to train.
It was cheaper and faster to train the neural network.
In addition, a network with fewer weights may be faster to train.
The training time of a cascade depends on a lot of parameters: number of training samples, number of levels, implementation (C++/MATLAB), …Rather than giving precise training times to compare a cascade and a McCascade, rough estimates are given here to emphasize the fact that a McCascade is faster to train than a cascade.
It is also much faster to train the supervised output layer of this network than to train a BP network, since the output layer only learns the labeling of the classes (based on the cluster boundaries internally identified by the SOM).
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While Regression Tree is very fast to train, it is limited to encoder regions with axis-parallel splits.
In engineering, kriging is widely used because it is fast to train and is generally more accurate than other types of surrogate models.
The proposed method: achieves competitive results when compared with other facial expression recognition methods – 96.76% of accuracy in the CK+ database – it is fast to train, and it allows for real time facial expression recognition with standard computers.
Moreover, the classifiers must be fast to train.
NB is popular in real-time (or near real-time) systems as it is both fast to train and fast to classify.
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