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ensmallen [7] is a high quality C++ library for non linear numerical optimizer, it uses Armadillo or bandicoot for linear algebra and it is used by mlpack to provide optimizer for training machine learning algorithms. Similar to mlpack, ensmallen is a header-only library and supports custom behavior using callbacks functions allowing the users ...
In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. [3] All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.
This section discusses strategies of extending the existing binary classifiers to solve multi-class classification problems. Several algorithms have been developed based on neural networks, decision trees, k-nearest neighbors, naive Bayes, support vector machines and extreme learning machines to address multi-class classification problems ...
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. [1]
scikit-learn (a free and open-source machine learning library for the Python programming language). Weka (a free and open-source data-mining suite, contains many decision tree algorithms), Notable commercial software: MATLAB, Microsoft SQL Server, and; RapidMiner, SAS Enterprise Miner, IBM SPSS Modeler,
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Neighbourhood components analysis is a supervised learning method for classifying multivariate data into distinct classes according to a given distance metric over the data. . Functionally, it serves the same purposes as the K-nearest neighbors algorithm and makes direct use of a related concept termed stochastic nearest neighbo