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In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, [1] although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary.
In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features.Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.
Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels. As an example, a sample instance might be a natural language sentence, and the output label is an annotated parse tree. Training a classifier consists of ...
Classification systems are systems with a distribution of classes created according to common relations or affinities. Wikimedia Commons has media related to Classification systems . See also:
Classification Tree for a Database System. For a database system, test design has to be performed. Applying the classification tree method, the identification of test relevant aspects gives the classifications: User Privilege, Operation and Access Method. For the User Privileges, two classes can be identified: Regular User and Administrator User.
A step-wise schematic illustrating a generic Michigan-style learning classifier system learning cycle performing supervised learning. Keeping in mind that LCS is a paradigm for genetic-based machine learning rather than a specific method, the following outlines key elements of a generic, modern (i.e. post-XCS) LCS algorithm.
Binary classification is the task of classifying the elements of a set into one of two groups (each called class). Typical binary classification problems include: Medical testing to determine if a patient has a certain disease or not; Quality control in industry, deciding whether a specification has been met;
For example, a classifier (for example k-means), takes a vector of features (decision variables) and outputs for each possible classification result the probability that the vector belongs to the class. This is usually used to take a decision (classify into the class with highest probability), but cascading classifiers use this output as the ...