<span>In today’s world, Diabetes is one of these diseases and now a big growing health problem. The techniques data mining have been widely applied to extract knowledge from medical databases. In this work, Medical Diagnosis system proposed for the ‎diagnosis diabetes in manner ‎that rapid cost-effective. three stages are ‎involved diagnosis (DDS) including: dataset constructing, preproce...

The heart is one of the human organs that has an important function to circulate blood throughout body. In caring for heart, must know how take care and check health conditions regularly do it early in order avoid dangerous diseases attack heart. this study, a disease diagnostic examination was carried out using machine learning with Naïve Bayes method. criteria used predicting study were gende...

In this paper, we introduce L1/Lp regularization of differences as a new regularization approach that can directly regularize models such as the naive Bayes classifier and (autoregressive) hidden Markov models. An algorithm is developed that selects values of the regularization parameter based on a derived stability condition. for the regularized naive Bayes classifier, we show that the method ...

This section describes a model for binary classification, Naive Bayes. Naive Bayes is a simple but important probabilistic model. It will be used as a running example in this note. In particular, we will first consider maximum-likelihood estimation in the case where the data is “fully observed”; we will then consider the expectation maximization (EM) algorithm for the case where the data is “pa...

In this paper, we introduce a new restricted Bayesian network classifier that extends naive Bayes by relaxing the conditional independence assumptions, and show that it is partly generative and partly discriminative. Experimental results show that the hybrid classifier performs better than a purely generative classifier (naive Bayes) or a purely discriminative classifier (Logistic Regression) a...

In Chinese information processing, Naive Bayes is a simple text classification method that is easily implemented. Its core is the realization of the calculating posterior probability algorithm and the effectively reducing dimension for feature words. This paper improved Naive Bayes text classification from the calculating posterior probability and the reducing dimension of feature words of text...

In this paper, we describe our system that participated in SemEval-2013, Task 2.B (sentiment analysis in Twitter). Our approach consists of adapting Naive Bayes probabilities in order to take into account prior knowledge (represented in the form of a sentiment lexicon). We propose two different methods to efficiently incorporate prior knowledge. We show that our approach outperforms the classic...

Abstract. The naive Bayes and maximum entropy approaches to text classification are typically discussed as completely unrelated techniques. In this paper, however, we show that both approaches are simply two different ways of doing parameter estimation for a common log-linear model of class posteriors. In particular, we show how to map the solution given by maximum entropy into an optimal solut...

We report on an empirical study of supervised learning algorithms that induce models to resolve the meaning of ambiguous words in text. We find that the Naive Bayesian classifier is as accurate as several more sophisticated methods. This is a surprising result since Naive Bayes makes simplifying assumptions about disambiguation that are not realistic. However, our results correspond to a growin...