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

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

CA : A Cancer Journal for Clinicians

We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

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Survey over image thresholding techniques and quantitative performance evaluation

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Abstaining classificaiton aims to reject to classify the easily misclassified examples, so it is an effective approach to increase the clasificaiton reliability and reduce the misclassification risk in the cost-sensitive applications. In such applications, different types of errors (false positive or false negative) usaully have unequal costs. And the error costs, which depend on specific applications, are usually unknown. However, current abstaining classification methods either do not distinguish the error types, or they need the cost information of misclassification and rejection, which are realized in the framework of cost-sensitive learning. In this paper, we propose a bounded-abstention method with two constraints of reject rates (BA2), which performs abstaining classification when error costs are unequal and unknown. BA2 aims to obtain the optimal area under the ROC curve (AUC) by constraining the reject rates of the positive and negative classes respectively. Specifically, we construct the receiver operating characteristic (ROC) curve, and stepwise search the optimal reject thresholds from both ends of the curve, untill the two constraints are satisfied. Experimental results show that BA2 obtains higher AUC and lower total cost than the state-of-the-art abstaining classification methods. Meanwhile, BA2 achieves controllable reject rates of the positive and negative classes.

Abstaining Classification When Error Costs are Unequal and Unknown.

A neuro-fuzzy approach for classification of snow cover and sea ice from the Special Sensor Microwave/Imager (SSM/I) satellite data is presented. The fuzzy c-means algorithm was used as a supervised clustering method for mapping the SSM/I data of known classes into fuzzy c-partitions from which to determine the fuzzy c-means for each class. A single-hidden-layer neural network was implemented to learn the fuzzy c-means and desired responses in terms of fuzzy memberships. After training, the neural network was applied as a fuzzy membership function to fuzzify any inputs of unknown SSM/I data and defuzzify the outputs into crisp classes for mapping snow and ice extent. Weekly snow and sea ice extent was tested and compared with the one derived from the National Snow and Ice Data Center (NSIDC).

A neuro-fuzzy approach for monitoring global snow and ice extent with the SSM/I

High-level synthesis is becoming increasingly important in the area of VLSI CAD. During the last decade, much research has been carried out in this area and some results have been achieved. This paper presents a survey on the state-of-the-art of current high-level synthesis research and applications. Besides basic knowledge, a comprehensive overview has been conducted among the various theories, methods, algorithms, and applications of the existing systems. Their advantages, limitations, and feasibilities are also described. Finally, some points on current status and future directions of high-level synthesis are addressed.

Heng-Da Cheng

Papers

Abstaining Classification When Error Costs are Unequal and Unknown.

A neuro-fuzzy approach for monitoring global snow and ice extent with the SSM/I

High-level synthesis: current status and future prospects

Incoherent radar spectrum processing using neural network

Fuzzy watershed method for image segmentation