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 ...

The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a design sample size that increases exponentially with the number of dimensions. A method was developed that combines classifications from marginal density functions using an additional classifier. Unlike voting methods,...

The Naive Bayesian classifier (NBC) is a well-known classification model that has simple structure, low training complexity, excellent scalability, and good performances. However, the NBC two key limitations: (1) it built upon strong assumption condition attributes are independent, which often does not hold in real-life, (2) handle continuous well. To overcome these limitations, this paper pres...

In the emerging field of toxicogenomics, microarray technology is used to read the level of gene expression in cells subjected to various toxic and non toxic agents. The amount of levels that are read from one single array is in the thousands, and when the number of arrays increases, statistical methods are needed to process the information. This master’s thesis documents the use of Bayesian st...

In this paper, we attempt to automatically annotate the Penn Chinese Treebank with semantic dependency structure. Initially a small portion of the Penn Chinese Treebank was manually annotated with headword and semantic dependency relations. An initial investigation is then done using a Naive Bayesian Classifier and some handcrafted rules. The results show that the algorithms and proposed approa...

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...