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===== Support Vector Machines ====== | ===== Support Vector Machines ====== | ||

- | In machine learning, [[https://en.wikipedia.org/wiki/Support_vector_machine|support vector machines]] (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked for belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. | + | In machine learning, [[wp>support vector machines]] (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked for belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. |

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. | In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. | ||

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===== Boosting ====== | ===== Boosting ====== | ||

- | [[https://en.wikipedia.org/wiki/Boosting_%28machine_learning%29|Boosting]] is a machine learning ensemble meta-algorithm for reducing bias primarily and also variance[1] in supervised learning, and a family of machine learning algorithms which convert weak learners to strong ones.[2] Boosting is based on the question posed by Kearns and Valiant (1988, 1989):[3][4] Can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification. | + | [[wp>Boosting]] is a machine learning ensemble meta-algorithm for reducing bias primarily and also variance[1] in supervised learning, and a family of machine learning algorithms which convert weak learners to strong ones.[2] Boosting is based on the question posed by Kearns and Valiant (1988, 1989):[3][4] Can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification. |

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===== Deep Learning ====== | ===== Deep Learning ====== | ||

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