Bayesian Classifiers are widely used in machine learning supervised models where there is a reasonable reliability in the dependent variable. This work aims to create a risk measurement model of companies that negotiate with the government using indicators grouped into four risk dimensions: operational capacity, history of penalties and findings, bidding profile, and political ties. It is expec...

We propose a lawn weed detection method modified from our previous work, i.e., Bayesian classifier based method. The proposed method employs features calculated from not only the edge-strength of weed/lawn textures but also color information of RGB. Instead of using Bayesian classifier, we exploit more sophisticated classifier, i.e., supportvector machine, for detecting weeds. After weed detect...

A new statistical approach based on global typographical features is proposed to the widely neglected problem of font recognition. It aims at the identification of the typeface, weight, slope and size of the text from an image block without any knowledge of the content of that text. The recognition is based on a multivariate Bayesian classifier and operates on a given set of known fonts. The ef...

This article describes the systems submitted by the Citius Ixa Imaxin team to the Discriminating Similar Languages Shared Task 2016. The systems are based on two different strategies: classification with ranked dictionaries and Naive Bayes classifiers. The results of the evaluation show that ranking dictionaries are more sound and stable across different domains while basic bayesian models perf...

Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophisticated classifiers, even in view of inaccuracies in their parameters. In this paper, we study the effects of such parameter inaccuracies by investigating the sensitivity functions of a naive Bayesian network. We show that, as a consequence of the network’s independence properties, these sensitivi...

In this paper, we propose a novel face photo retrieval system using sketch drawings. By transforming a photo image into a sketch, we reduce the difference between photo and sketch significantly, thus allow effective matching between the two. To improve the synthesis performance, we separate shape and texture information in a face photo, and conduct transformation on them respectively. Finally a...

In this paper, we present an automatic system that is able to forecast the appearance of a soccer highlight, and annotate it, based on MPEG features; processing is performed in strict real time. A probabilistic framework based on Bayes networks is used to detect the most significant soccer highlights. Predictions are validated by different Bayes networks, to check the outcome of forecasts.

We consider the complexity of determining whether two sets of probability distributions result in different plans or significantly different plan success for Bayes nets. Subarea: belief networks.

Abst rac t . A new classification algorithm called VFI (for Voting Feature Intervals) is proposed. A concept is represented by a set of feature intervals on each feature dimension separately. Each feature participates in the classification by distributing real-valued votes among classes. The class receiving the highest vote is declared to be the predicted class. VFI is compared with the Naive B...

Children acquiring languages with noun classes (grammatical gender) have ample statistical information available that characterizes the distribution of nouns into these classes, but their use of this information to classify novel nouns differs from the predictions made by an optimal Bayesian classifier. We propose three models that introduce uncertainty into the optimal Bayesian classifier and ...