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research [2015/07/06 17:25]
Frederic Precioso [Boosting]
research [2015/07/06 17:27] (current)
Frederic Precioso [Boosting]
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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.+[[https://​en.wikipedia.org/​wiki/​Boosting_(machine_learning)|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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