방승양 교수

Proposes a data classification method based on the tolerant rough set that extends the existing equivalent rough set. A similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity threshold value is very important for accurate classification. So, we determine it optimally by using the genetic algorithm (GA), wher

While the support vector machine (SVM) can provide a good generalization performance, the classification result of the SVM is often far from the theoretically expected level in practical implementation because they are based on approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use an SVM ensemble with bagging (bootstrap aggregating) or boosting. In bagging, each individual SVM is trained independ

Variational Bayesian Expectation-Maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has been a standard technique for practical Bayesian inference. In this paper, we introduce a more general approximate inference framework for conjugate-exponential family models, which we call Latent-Space Variational Bayes (LSVB). In this approach, we integrate out model parameters in an exact way, leaving only the laten

This paper proposes a real-time vehicle management system using a vehicle tracking and a car plate number identification technique. The system uses two cameras: one for tracking vehicles and another for capturing LP (license plate). We track the vehicles by applying the CONDENSATION algorithm over the vehicle's movement image captured from the first camera. To render the CONDENSATION algorithm more effective, we build a discrete vehicle shape model by training vehicle patterns with a SOM (self o