Linear Classifiers use object’s characteristics to predict which class (or group) it belongs to. It achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object’s characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector. Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (features), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use. 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. Also, linear classifiers often work very well when the number of dimensions in x is large, as in document classification, where each element in is typically the number of occurrences of a word in a document. In such cases, the classifier should be well-regularized. There are two broad classes of methods for determining the parameters of a linear classifier They can be Generative and Discriminative models.
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