in Continuous Domains Tapio Elomaa and Juho Rousu Department of Computer S ien e, University of Helsinki, Finland {elomaa,rousu} s.helsinki.fi Abstra t. Naïve Bayesian lassi ers assume the onditional independen e of attribute values given the lass. Despite this in pra ti e often violated assumption, these simple lassi ers have been found e ient, e e tive, and robust to noise. Dis retization of ...

In this paper, we introduce a new learning algorithm for adaptive intrusion detection using boosting and naïve Bayesian classifier, which considers a series of classifiers and combines the votes of each individual classifier for classifying an unknown or known example. The proposed algorithm generates the probability set for each round using naïve Bayesian classifier and updates the weights of ...

In this paper, we employed Naïve Bayes, Markov blanket and Tabu search to rank web services. The Bayesian Network is demonstrated on a dataset taken from literature. The dataset consists of 364 web services whose quality is described by 9 attributes. Here, the attributes are treated as criteria, to classify web services. From the experiments, we conclude that Naïve based Bayesian network perfor...

In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective ...

Highlights • Orders-of-magnitude improvement in approximate Bayesian inference efficiency • Bitstream autocorrelation limits inference approximation accuracy • Autocorrelation successfully mitigated to improve Bayesian inference approximation • Approximate Bayesian inference efficiently performed in hardware 2 Abstract Advancements in autonomous robotic systems have been impeded by the lack of ...

The Markov Blanket Bayesian Classifier is a recentlyproposed algorithm for construction of probabilistic classifiers. This paper presents an empirical comparison of the MBBC algorithm with three other Bayesian classifiers: Naïve Bayes, Tree-Augmented Naïve Bayes and a general Bayesian network. All of these are implemented using the K2 framework of Cooper and Herskovits. The classifiers are comp...

Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models (classifiers) than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of...

In this paper we address the problem of offline Arabic handwriting word recognition. Offline recognition of handwritten words is a difficult task due to the high variability and uncertainty of human writing. The majority of the recent systems are constrained by the size of the lexicon to deal with and the number of writers. In this paper, we propose an approach for multi-writers Arabic handwrit...

This work augments the Naïve Bayesian learning algorithm with a second training phase in an attempt to improve its classification accuracy. This is achieved by finding more accurate estimations of the needed probability terms. This approach helps in dealing with the problem of the lack of training data. Unlike many previous approaches that deal with this problem, the proposed method is an eager...