Partial Histogram Bayes Learning Algorithm for Classification Applications
نویسندگان
چکیده
منابع مشابه
An Improved Learning Algorithm for Augmented Naive Bayes
Data mining applications require learning algorithms to have high predictive accuracy, scale up to large datasets, and produce compre-hensible outcomes. Naive Bayes classiier has received extensive attention due to its eeciency, reasonable predictive accuracy, and simplicity. However , the assumption of attribute dependency given class of Naive Bayes is often violated, producing incorrect proba...
متن کاملNaive-Bayes for Sentiment Classification
This report details the findings in building a naive Bayes sentiment classifier for a IMDB movie-review data set using Scala and ScalaNLP. We studied the unigram or bagof-words Bernoulli and Multinomial models and a number of different feature selection techniques, including term frequency, mutual information and Chi-squared. 1. DATA CORPUS The corpus contains of 2000 rated movie reviews, compr...
متن کاملHierarchical Bayes for Text Classification
Naive Bayes models have been very popular in several classification tasks. In this paper we study the application of these models to classification tasks where the data is sparse i.e., a large number of possible outcomes do not appear in the data. Traditionally point estimates of the model parameters and in particular, point estimates based on the Laplace’s rule have been popular for such spars...
متن کاملMacro for Naïve Bayes Classification
The supervised classification also known as pattern recognition, discrimination, or supervised learning consists of assigning new cases to one of a set of pre-defined classes given a sample of cases for which the true classes are known. The Naïve Bayes (NB) technique of supervised classification has become increasingly popular in the recent years. Despite its unrealistic assumption that feature...
متن کاملPartial Intrinsic Bayes Factor
We have developed a new model selection criteria, the partial intrinsic Bayes factor, which is designed for cases when we select a model among a small number of candidate models. For example, we can choose only a few candidate models after exploring scatter plots. Based on this motivation, the partial intrinsic Bayes factor is developed. By simulation study, we showed that PIBF performs better ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Engineering & Technology
سال: 2018
ISSN: 2227-524X
DOI: 10.14419/ijet.v7i4.11.20787