Definition
If the input feature vector to the classifier is a real vector, then the output score is
where is a real vector of weights and f is a function that converts the dot product of the two vectors into the desired output. (In other words, is a one-form or linear functional mapping onto R.) The weight vector is learned from a set of labeled training samples. Often f is a simple function that maps all values above a certain threshold to the first class and all other values to the second class. A more complex f might give the probability that an item belongs to a certain class.
For a two-class classification problem, one can visualize the operation of a linear classifier as splitting a high-dimensional input space with a hyperplane: all points on one side of the hyperplane are classified as "yes", while the others are classified as "no".
A linear classifier is often used in situations where the speed of classification is an issue, since it is often the fastest classifier, especially when is sparse. However, decision trees can be faster. Also, linear classifiers often work very well when the number of dimensions in is large, as in document classification, where each element in is typically the number of occurrences of a word in a document (see document-term matrix). In such cases, the classifier should be well-regularized.
Read more about this topic: Linear Classifier
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