As a real-world application, the distributed one-dimensional density estimation can be used for naive Bayes classification in the Internet of Things (IoT) ...
In this paper, we follow an alternative to the sub-sampling approach by proposing the nested Log-Poly model. This model provides an accurate density estimation.
Oct 14, 2020 · Beigy, A distributed density estimation algorithm and its application to naive Bayes classification, Applied Soft Computing. Journal (2020) ...
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We compare the accuracy and the communication load of naive Bayes classifier using nested Log-Poly and other related density estimators on several real datasets ...
There is a well-known algorithm called the Naive Bayes algorithm. Here the basic assumption is that all the variables are independent given the class label.
Our goal is to determine the values for the parameters in M. • We can do this by maximizing the probability of generating the observed samples.
Mar 23, 2024 · Gaussian Naive Bayes is a popular machine learning algorithm known for its simplicity and effectiveness in classification tasks.
Jan 2, 2020 · A KDE weights a defined density around each observation xr equally first. In this regard, a kernel function K is needed – eg a normal, triangular, epanechnikov ...
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Naive Bayes is a classification algorithm that applies density estimation to the data. The algorithm leverages Bayes theorem, and (naively) assumes that the ...
Jan 10, 2020 · In this tutorial, you will discover the Naive Bayes algorithm for classification predictive modeling.