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

We propose an iterative algorithm for simultaneously estimating the motion field and high resolution frames from a compressed low resolution video sequence. The algorithm exploits the existing correlation between high and low resolution frames and information provided by the encoder, such as coding modes and motion vectors (when available), to obtain a higher resolution frame. The performance of the algorithm is demonstrated experimentally.

Simultaneous motion estimation and resolution enhancement of compressed low resolution video

https://decsai.ugr.es/vip/files/journals/2016DSPSVVMK.pdf

Compressive sensing super resolution from multiple observations with application to passive millimeter wave images

Serum HER2 extracellular domain predicts an aggressive clinical outcome and biological PSA response in hormone-independent prostate cancer patients treated with docetaxel

We will describe here the learning algorithms we have implemented in CASTLE, (Causal Structures From Inductive Learning), to learn about causal structures from examples. A brief introduction to the software ifself and a description of what we intend to develop and implement in CASTLE are also given. Finally, the use of CASTLE is illustrated on a simple example.

Learning with CASTLE

In this article, we address the problem of obtaining a high-resolution (HR) image from a compressed low-resolution (LR) video sequence. Motion information plays a critical role in solving this problem, and we determine which pixels in the sequence provide useful information for calculating the high-resolution image. The bit stream of hybrid motion compensated video compression methods includes low-resolution motion-compensated images; we therefore also study which pixels in these images should be used to increase the quality of the reconstructed image. Once the useful (observable) pixels in the low-resolution and motion-compensated sequences have been detected, we modify the acquisition model to only account for these observations. The proposed approach is tested on real compressed video sequences and the improved performance is reported. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 58–66, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20008

/pdf/high-resolution-images-from-compressed-low-resolution-video-43qtlrt3ff.pdf

Rafael Molina

Papers

Simultaneous motion estimation and resolution enhancement of compressed low resolution video

Compressive sensing super resolution from multiple observations with application to passive millimeter wave images

Serum HER2 extracellular domain predicts an aggressive clinical outcome and biological PSA response in hormone-independent prostate cancer patients treated with docetaxel

Learning with CASTLE

High-Resolution Images from Compressed Low-Resolution Video: Motion Estimation and Observable Pixels