This paper proposes a novel Bayesian method for the dictionary learning (DL) based classification using Beta-Bernoulli process. We utilize this non-parametric Bayesian technique to learn jointly the sparse codes, the dictionary, and the classifier together. Existing DL based classification approaches only offer point estimation of the dictionary, the sparse codes, and the classifier and can the...

The synthesis of an effective multi-category nonlinear classifier with the capability to output calibrated posterior probabilities to enable post-processing is of great significance in practical recognition situations in that the posterior probability reflects the assessment uncertainty. In this paper, a multi-scale nonparametric and parametric hybrid recognition strategy is developed for this ...

We present a method of handwritten numeral recognition, where we introduce hierarchical Gabor features (HGFs) and construct a Bayesian network classifier that encodes the dependence between HGFs. We extract HGFs in such a way that they represent different levels of information which are structured such that the lower the level is, the more localized information they have. At each level, we choo...

The naive Bayes classifier has proved to be very effective on many real data applications. Its performance usually benefits from an accurate estimation of univariate conditional probabilities and from variable selection. However, although variable selection is a desirable feature, it is prone to overfitting. In this paper, we introduce a Bayesian regularization technique to select the most prob...

For the characteristics of large number, fast change, high cost of random access of data stream, this paper proposes a Bayesian classification data mining algorithm based on incremental storage tree to handle the problems. Use sliding window to process data stream and divide it into several basic units, apply Principal component analysis (PCA) to compress the data from window and produce dynami...

Feature selection has proved to be an effective way to reduce the model complexity while giving a relatively desirable accuracy, especially, when data is scarce or the acquisition of some feature is expensive. However, the single selected model may not always generalize well for unseen test data whereas other models may perform better. Bayesian Model Averaging (BMA) is a widely used approach to...

Accuracy is one of the major issues for a classifier. Currently there exist a range of classifiers with different degrees of accuracy directly related to computational complexity. In this paper we are presenting an approach to improve the classification accuracy of an existing PTree based Bayesian classification technique. The new approach has increased the granularity between two conditional p...

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for ...

Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for ...

We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems infeasible, we are able to approximate this density by exploiting statistics of the image database domain. Unlike previous approaches that assume an arbitrary distribution for the unconditional density of the feature vec...