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Fast to train (single scan).
Moreover, the classifiers must be fast to train.
Linear SVMs are very fast to train, but also limited to use an inner product to compare descriptors.
While Regression Tree is very fast to train, it is limited to encoder regions with axis-parallel splits.
We chose a Naive Bayesian classifier because of its advantages and our type of data as: Easy to implement Fast to train (single scan).
NB is popular in real-time (or near real-time) systems as it is both fast to train and fast to classify.
Similar(49)
It was cheaper and faster to train the neural network.
In addition, a network with fewer weights may be faster to train.
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.
Firstly, linear SVMs are considerably faster to train and have fewer parameters to optimize than nonlinear SVMs a significant advantage in a comprehensive study.
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