In this paper we present an active learning procedure for the two-class supervised classification problem. The utilized methodology exploits the Bayesian modeling and inference paradigm to tackle the problem of kernel-based data classification. This Bayesian methodology is appropriate for both finite and infinite dimensional feature spaces. Parameters are estimated, using the kernel trick, foll...

We introduce a simple empirical order-based greedy heuristic for learning discriminative Bayesian network structures. We propose two metrics for establishing the ordering of N features. They are based on the conditional mutual information. Given an ordering, we can find the discriminative classifier structure with O (Nq) score evaluations (where constant q is the maximum number of parents per n...

Abstract Classification of real-world data poses a number of challenging problems. Mismatch between classifier models and true data distributions on one hand and the use of approximate inference methods on the other hand all contribute to inaccurate classification. Recent work on boosting by Schapire et al. and additive probabilistic models by Hastie et al. have shown that improved classificati...

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...

Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography-mass spectrometry (LC-MS)....

Classification is an open, old and basic problem in many domains. Recently, a lot of new methods come forth, such as Bayesian Networks. Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Bayesian Networks Augmented Naive Bayes (BAN) to texture classification of aerial image and propose a new...

Even though hidden Markov models (HMMs) have been used for the automatic classification of volcanic earthquakes, their usage has been so far limited to the Bayesian scheme. Recently proposed alternatives, proven in other application scenarios, consist in building HMMinduced vector spaces where discriminative classification techniques can be applied. In this paper, a simple vector space is induc...

Cancer detection is one of the important research topics in medical science. In bioinformatics age, gene expression data can be used for the cancer detection. Data mining techniques, such as pattern association, classification and clustering, are now frequently applied in cancer and gene expressions correlation studies. Classification is very important among these techniques of data mining. Her...

We introduce Bayesian Eigenobjects (BEOs), a novel object representation that is the first technique able to perform joint classification, pose estimation, and 3D geometric completion on previously unencountered and partially observed query objects. BEOs employ Variational Bayesian Principal Component Analysis (VBPCA) directly on 3D object representations to create generative and compact probab...

Recognizing the successes of treed Gaussian process (TGP) models as an interpretable and thrifty model for nonstationary regression, we seek to extend the model to classification. By combining Bayesian CART and the latent variable approach to classification via Gaussian processes (GPs), we develop a Bayesian model averaging scheme to traverse the full space of classification TGPs (CTGPs). We il...