The instance-based k-nearest neighbor algorithm (KNN)[1] is an effective classification model. Its classification is simply based on a vote within the neighborhood, consisting of k nearest neighbors of the test instance. Recently, researchers have been interested in deploying a more sophisticated local model, such as naive Bayes, within the neighborhood. It is expected that there are no strong ...

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We’ll start out with a very simple learning algorithm: multinomial Naive Bayes. Our implementation is in Table 1. Each training example is a labeled document d = (i, y, (w1, . . . , wni)) with an identifier i, a label y from a small set Y = {y1, . . . , yK}, and a “bag of words”. The bag of words are wj’s, encoded here as a list of strings, so that wj is the word/token at position j of document...

2 Getting started 2 2.1 What is clustering? . . . . . . . . . . . . . . . . . . . 2 2.2 Target data . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3 Naive Bayes models . . . . . . . . . . . . . . . . . . . 3 2.3.1 Overall structure . . . . . . . . . . . . . . . . 3 2.3.2 Attribute distribution (in general) . . . . . . . 3 2.3.3 Attribute distribution (in detail) . . . . . . . . 3 2.4 Runni...

We investigate the theoretical performance of Bayes factor estimators in wavelet regression models with independent and identically distributed errors that are not necessarily normally distributed. We compare these estimators in terms of their frequentist optimality in Besov spaces for a wide variety of error and prior distributions. Furthermore, we provide sufficient conditions that determine ...