We compare two strategies for training connectionist (as well as nonconnectionist) models for statistical pattern recognition. The probabilistic strategy is based on the notion that Bayesian discrimination (i.e .• optimal classification) is achieved when the classifier learns the a posteriori class distributions of the random feature vector. The differential strategy is based on the notion that...

This paper describes the modeling of a biomass-based weed-crop competitiveness classification process based on classification rules extracted from Bayesian network classifiers. Two Bayesian network classifiers are employed, namely an unrestricted Bayesian network classifier and a näıve Bayes classifier. The BayesRule algorithm is then used to extract a set of rules from each Bayesian network cl...

Recent works on ensemble methods like Adaptive Boosting have been applied successfully in many problems. Ada-Boost algorithm running on a given weak learner several times on slightly altered data and combining the hypotheses in order to achieve higher accuracy than the weak learner. This paper presents an expert system that boosts the performance of an ensemble of classifiers. In, Boosting, a s...

Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions. In this paper we introduce a classifier taking as basis the TAN models and taking into account uncertainty in model selection. To do this we introduce decomposable distributions over TANs and show that the expression ...

To automatically assess young children’s reading skills as demonstrated by isolated words read aloud, we propose a novel structure for a Bayesian Network classifier. Our network models the generative story among speech recognition-based features, treating pronunciation variants and reading mistakes as distinct but not independent cues to a qualitative perception of reading ability. This Bayesia...

We compare the performance of several machine learning algorithms in the problem of prognostics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for t...

This paper presents an approach to automatic recognition of emotional states from audio-visual bimodal signals using semi-coupled hidden Markov model and error weighted classifier combination for Human-Computer Interaction (HCI). The proposed model combines a simplified state-based bimodal alignment strategy and a Bayesian classifier weighting scheme to obtain the optimal solution for audio-vis...

Bayesian classifiers such as Naive Bayes or Tree Augmented Naive Bayes (TAN) have shown excellent performance given their simplicity and heavy underlying independence assumptions. In this paper we introduce a classifier taking as basis the TAN model and taking into account uncertainty in model selection. To do this we introduce decomposable distributions over TANs and show that they allow the e...

The naive Bayesian classifier is a simple and effective classification method, which assumes a Bayesian network in which each attribute has the class label as its only one parent. But this assumption is not obviously hold in many real world domains. Tree-Augmented Naive Bayes (TAN) is a state-of-the-art extension of the naive Bayes, which can express partial dependence relations among attribute...

In this paper an Adaptive Bayesian Contextual classification procedure that utilizes both spectral and spatial interpixel dependency contexts in estimation of statistics and classification is proposed. Essentially, this classifier is the constructive coupling of an adaptive classification procedure and a Bayesian contextual classification procedure. In this classifier, the joint prior probabili...