Gaussian classifier

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• Naive Bayes classifier — A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes theorem with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be independent feature model . In… …   Wikipedia

• Linear classifier — In the field of machine learning, the goal of classification is to group items that have similar feature values, into groups. A linear classifier achieves this by making a classification decision based on the value of the linear combination of… …   Wikipedia

• Perceptron — Perceptrons redirects here. For the book of that title, see Perceptrons (book). The perceptron is a type of artificial neural network invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt.[1] It can be seen as the simplest… …   Wikipedia

• Support vector machine — Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Viewing input data as two sets of vectors in an n dimensional space, an SVM will construct a separating hyperplane in that… …   Wikipedia

• Supervised learning — is a machine learning technique for learning a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs. The output of the functioncan be a continuous value (called regression), or… …   Wikipedia

• Statistical classification — See also: Pattern recognition See also: Classification test In machine learning, statistical classification is the problem of identifying the sub population to which new observations belong, where the identity of the sub population is unknown, on …   Wikipedia

• Artificial intelligence — AI redirects here. For other uses, see Ai. For other uses, see Artificial intelligence (disambiguation). TOPIO, a humanoid robot, played table tennis at Tokyo International Robot Exhibition (IREX) 2009.[1] Artificial intelligence ( …   Wikipedia

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• One-shot learning — is an object categorization problem of current research interest in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one shot… …   Wikipedia

• Boosting methods for object categorization — Given images containing various known objects in the world, a classifier can be learned from them to automatically categorize the objects in future images. Simple classifiers built based on some image feature of the object tend to be weak in… …   Wikipedia

• Shape context — is the term given by Serge Belongie and Jitendra Malik to the feature descriptor they first proposed in their paper Matching with Shape Contexts in 2000cite conference author = S. Belongie and J. Malik title = Matching with Shape Contexts url =… …   Wikipedia