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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.
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] A greedy optimisation procedure and thus fast version were subsequently developed.
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 ...
Logistic regression; Ordinary least squares regression (OLSR) Linear regression; Stepwise regression; Multivariate adaptive regression splines (MARS) Regularization algorithm Ridge regression; Least Absolute Shrinkage and Selection Operator (LASSO) Elastic net; Least-angle regression (LARS) Classifiers. Probabilistic classifier. Naive Bayes ...
Support vector regression (prediction) with different thresholds ε. As ε increases, the prediction becomes less sensitive to errors. A version of SVM for regression was proposed in 1996 by Vladimir N. Vapnik, Harris Drucker, Christopher J. C. Burges, Linda Kaufman and Alexander J. Smola. [40] This method is called support vector regression (SVR).
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.
scikit-learn, an open source machine learning library for Python; Orange, a free data mining software suite, module Orange.ensemble; Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost
Using Ohm's law as an example, a regression could be performed with voltage as input and current as an output. The regression would find the functional relationship between voltage and current to be R {\displaystyle R} , such that V = I R {\displaystyle V=IR} Classification problems are those for which the output will be an element from a ...