Margin classifier & Support vector machine - Unionpedia, the concept map

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Difference between Margin classifier and Support vector machine

Margin classifier vs. Support vector machine

In machine learning, a margin classifier is a classifier which is able to give an associated distance from the decision boundary for each example. In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.

Generalization error

For supervised learning applications in machine learning and statistical learning theory, generalization errorMohri, M., Rostamizadeh A., Talwakar A., (2018) Foundations of Machine learning, 2nd ed., Boston: MIT Press (also known as the out-of-sample error or the risk) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data.

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Hyperplane separation theorem

In geometry, the hyperplane separation theorem is a theorem about disjoint convex sets in n-dimensional Euclidean space.

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Linear classifier

In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to.

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Machine learning

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions.

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Margin (machine learning)

In machine learning the margin of a single data point is defined to be the distance from the data point to a decision boundary.

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Perceptron

In machine learning, the perceptron (or McCulloch–Pitts neuron) is an algorithm for supervised learning of binary classifiers.

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Statistical classification

When classification is performed by a computer, statistical methods are normally used to develop the algorithm.

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