Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. In general, text classification plays an important role in information extraction and summarization, text retrieval, and question-answering. The Multinomial Bayesian Classifier has traditionally been a focus of res...

It has always been a hotspot and difficult point in remote sensing to identify interesting geographical objects from remote sensing images. To reduce the independence between the random variables in the network Bayesian classifier model and to improve the classification performance, a causality-based network Bayesian classifier is suggested in this paper. In this model, the improved genetic alg...

Electronic devices endowed with camera platforms require new and powerful machine vision applications, which commonly include moving object detection strategies. To obtain high-quality results, the most recent strategies estimate nonparametrically background and foreground models and combine them by means of a Bayesian classifier. However, typical classifiers are limited by the use of constant ...

Relevance feedback is a powerful technique in contentbased image retrieval (CBIR) and has been an active research area for the past few years. In this paper, we propose a new relevance feedback approach based on Bayesian classifier and it treats positive and negative feedback examples with different strategies. For positive examples, a Bayesian classifier is used to determine the distribution o...

In this paper, we address the problem of recognizing human activities, such as {Active, Inactive, Walking, Running, Fighting} from video sequences, with a particular emphasis on the problems of feature selection, data modeling and classifier structure. The need for such systems is increasing everyday, with the number of (hundreds or thousands) of surveillance cameras deployed in public spaces. ...

Quantitative or qualitative characterization of the drug-like features of known drugs may help medicinal and computational chemists to select higher quality drug leads from a huge pool of compounds and to improve the efficiency of drug design pipelines. For this purpose, the theoretical models for drug-likeness to discriminate between drug-like and non-drug-like based on molecular physicochemic...

This paper presents novel methods for classifying images based on knowledge discovered from annotated images using WordNet. The novelty of this work is the automatic class discovery and the classifier combination using the extracted knowledge. The extracted knowledge is a network of concepts (e.g., image clusters and word-senses) with associated image and text examples. Concepts that are simila...

Bayesian networks are models for uncertain reasoning which are achieving a growing importance also for the data mining task of classification. Credal networks extend Bayesian nets to sets of distributions, or credal sets. This paper extends a state-of-the-art Bayesian net for classification, called tree-augmented naive Bayes classifier, to credal sets originated from probability intervals. This...

handwritten digit recognition can be categorized as a classification problem. probabilistic neural network (pnn) is one of the most effective and useful classifiers, which works based on bayesian rule. in this paper, in order to recognize persian (farsi) handwritten digit recognition, a combination of intelligent clustering method and pnn has been utilized. hoda database, which includes 80000 p...

In this paper, we present a case study of interpreting paddy distributions of three counties on Northern Taiwan during two crop seasons on year 2000 using multitemporal imageries together with cadastre GIS by Bayesian posteriori probability classifier. In order to integrating Bayesian conditional probability, priori probabilities of paddy's attributes were estimated from photogrammettric interp...