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	Naive Bayes	सपोर्ट वेक्टर मशीन (वर्गीकरण)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1997	1995
प्रवर्तक	Mitchell, T. M. (textbook treatment)	Cortes, C. & Vapnik, V.
प्रकार	Probabilistic classifier (Bayes' theorem with conditional independence)	Maximum-margin classifier (kernel method)
मौलिक स्रोत	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
उपनाम	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
संबंधित	4	5
सारांश	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.