classifier

On the national level, there only needs to be one classifier.

Many classifiers can give an associated margin for each example.

This will result in C classifiers, whose results are combined.

It is a more general version of the linear classifier.

How to make the classifier adaptive over time becomes a big issue.

Use the test set only for the final performance comparison of the trained classifiers.

Such classifiers can be used for face recognition or texture analysis.

He formerly worked as a film classifier and a market researcher.

Determining a suitable classifier for a given problem is still more an art than science.

However, only some classifiers utilize information of the margin while learning from a data set.

A linear classifier can then separate the data, as shown in the third figure.

However, the classifier can only be as good as the given training data.

Speed: What is the computational cost of using the classifier?

Choose L to be the number of individual classifiers in the ensemble.

All classifiers are linked in a chain through feature space.

Most nouns have one or more particular classifiers associated with them.

This is repeated until no weak classifiers to add remain.

The new resulting classifier will then be applied to the whole sample set.

In essence, building a classifier brings model selection with it.

A Gain of 1 would indicate a classifier that is not better than random.

More information on training and testing classifiers is at statistical classification.

There are several status groups used by classifiers that assist in classification.

Generally only the labels the classifier is most confident of are added at each step.

There may be hundreds of classifiers in the population.

Tables 2, 5, 6 and 7, however, may only be applied by the classifier where instructed.