Weak Learner Machine Learning

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Perhaps the most surprising of these applications is the derivation of a new application for “boosting”, i.e., converting a “weak” PAC learning .Boosting is a machine learning ensemble meta algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones..Weak learners are basically thresholds for each feature. One simple example is a level decision tree called decision stump applied in .

AdaBoost, short for Adaptive Boosting, is a machine learning meta algorithm formulated by Yoav Freund and Robert Schapire, who won the Godel Prize for their work. It can be used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms ‘weak learners’ is combined into a weighted sum that represents the final output .Deep learning also known as deep structured learning or hierarchical learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task specific algorithms.Learning can be supervised, semi supervised or unsupervised Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been .Visit our free site designed especially for learners and teachers of Spanish SpanishCentral.com “.

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