The naive Bayes classiier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classiication, focusing on the distributional assumptions made about word occurrences in documents.

Class binarizations are effective methods that break multi-class problem down into several 2class or binary problems to improve weak learners. This paper analyzes which effects these methods have if we choose a Naive Bayes learner for the base classifier. We consider the known unordered and pairwise class binarizations and propose an alternative approach for a pairwise calculation of a modified...

This paper presents a method to assign function tags based on a Naive Bayes approach. The method takes as input a parse tree and labels certain constituents with a set of functional marks such as logical subject, predicate, etc. The performance reported is promising, given the simplicity of a Naive Bayes approach, when compared with similar work.

Entrapment detection is a prerequisite for planetary rovers to perform autonomous rescue procedure. In this study, rover entrapment and approximated entrapment criteria are formally defined. Entrapment detection using Naive Bayes classifiers is proposed and discussed along with results from experiments where the Naive Bayes entrapment detector is applied to AutoKralwer rovers. And final conclus...

In recent years, mixture models have found widespread usage in discovering latent cluster structure from data. A popular special case of finite mixture models are naive Bayes models, where the probability of a feature vector factorizes over the features for any given component of the mixture. Despite their popularity, naive Bayes models suffer from two important restrictions: first, they do not...

When modelling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous works have solved the problem by discretizing them with the consequent loss of information. Another common alternative assumes that the data are generated by a Gaussian distribution (parametric approach), such as conditional Gaussian networks, wit...

1 Naive Bayes, 20 points Problem 1. Basic concepts, 10 points Naive Bayes reduces the number of parameters that must be estimated for a Bayesian classifier, by making a conditional independence assumption when modeling P (X|Y). The definition for conditional independence is the following: Given this definition, please answer the following questions:

2 Naive Bayes Classification 3 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Posterior Probabilities . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Class-conditional Probabilities . . . . . . . . . . . . . . . . . . . 5 2.4 Prior Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.5 Evidence . . . . . . . . . . . . . . . . . . . . . . ...

This project aims to classify the optional practical training comments using a naive Bayes classifier. We demonstrate the effectiveness of the naive Bayes approach and further enhance its performance using a simplified form of an expectation maximisation algorithm. We explore how sentiments change over time, and also provide preliminary results that help in understanding how sentiments vary wit...

Designed for multi-relational explore and learn about important device data classification, and can be widely used in many fields. New classification algorithm Union, naive Bayes, which is the main function of what is known in the literature for the application of multiple classification Union relational environment. The results showed that naive Bayes achieves greater accuracy compared to exis...