다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.

/pdf/a-density-based-algorithm-for-discovering-clusters-in-large-1o5u8sf38m.pdf

A density-based algorithm for discovering clusters in large spatial Databases with Noise

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

High compression ratios for both still images and sequences of images are usually achieved by quantizing the block discrete cosine transform (BDCT) coefficients of the intensity or displaced frame different values. This block based processing and quantization yields images that exhibit annoying blocking artifacts. In this paper, we propose a method based on the hierarchical Bayesian approach for the reconstruction of BDCT compressed images and the estimation of the related parameters both at the coder and decoder. We examine how to combine the parameters estimated at the coder and decoder and test the method on real images.

Estimating and transmitting regularization parameters for reducing blocking artifacts

In this paper we present some results for automatic characterization of galaxies from digital images. In a rst approach we try to characterize spirals and elliptical galaxies as realizations of MRF with diierent interaction parameters. In a second approach we attempt the characterization using some points on the galaxy contour that represent the shape of the galaxy, more precesily those with highest local curvature. Then we reduce the dimensionality of the problem using the boundary to characterize a galaxy shape.

Non-supervised characterization of galaxies using different approaches

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, following the evidence Bayesian approach from the marginal distribution of the observations. The proposed active learning procedure uses a criterion based on the entropy of the posterior distribution of the adaptive parameters to select the sample to be included in the training set. A synthetic dataset as well as a real remote sensing classification problem are used to validate the followed approach.

/pdf/a-bayesian-active-learning-framework-for-a-two-class-530ethmuic.pdf

A Bayesian active learning framework for a two-class classification problem

In this paper the Super-Resolution (SR) image registration and reconstruction problem is studied within the Bayesian framework using a general sparse image prior combination. The representation of the proposed priors as Scale Mixtures of Gaussians (SMG), leads to the introduction of variational parameters, for which degenerate distributions are assumed. In the proposed method all the problem unknowns are automatically estimated using variational techniques. An experimental comparison between the proposed and state of the art methods has been performed, on both synthetic and real images.

/pdf/a-general-sparse-image-prior-combination-in-super-resolution-2edijo3xro.pdf

Rafael Molina

Papers

Estimating and transmitting regularization parameters for reducing blocking artifacts

Non-supervised characterization of galaxies using different approaches

A Bayesian active learning framework for a two-class classification problem

A general sparse image prior combination in super-resolution

From Information Fusion to Data Mining: Foreword