But before we can optimizep(c|x), we have to be able to compute it. For this, we useBayes’ theorem: Bayes theorem. Image by the Author. This is the Bayes part of naive Bayes. But now, we have the following problem: What arep(x|c) andp(c)?
Understanding how Gaussian naive Bayes classification works is best explained by example. Suppose, as in the demo program, the goal is to predict wheat seed species from seven predictor variables, and the item-to-predict has normalized values [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]. Gaussian N...
Naive Bayes Logistic Regression Support Vector Machines Gradient Boosting Decision Trees The K-means Clustering Algorithm Linear regression kNN Random Forests ECLAT Algorithm APRIORI Algorithm Association Rule Mining Algorithms Clustering Algorithms Instance-Based Learning Algorithms Artificial Neural Network (ANN...
Local naive bayes nearest neighbor for image classification. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 16–21 June 2012; pp. 3650–3656. [Google Scholar] Soriano-Sánchez, A.G.; Rodríguez-Licea, M.A.; Pérez-Pinal, F.J.;...
It is worth noting that for all scenarios we use the simulation data and covariance matrices explained in Section 2.5. The first four scenarios differ in f and T definitions, while they have the same covariance matrices. In the last scenario, the robustness of each measure to noise is ...