中文字幕 日韩 人妻 无码_亚洲无码电影_亚洲精品成人网站在线观看_久久99精品国产99久久6不卡,午夜亚洲国产理论片二级港台二级_黑人强伦姧人妻日韩HD_国产精品资源在线一区_大胸好大被揉捏好爽在线观看免费_全免费观看中文字幕三级_久久精品国产亚洲av瑜伽_亚洲中文字幕欧美岛国_又爽又黄无遮挡免费视频黄_国产成人高清在线播放_日韩一区中文无码 ,国产精品无码一级免费看A级毛激情_国产精品无码一区免费看_日韩亚洲av人人夜夜澡人人爽_亚洲最新av片不卡无码久久_中文字幕人妻第一区_国产欧美综合在线观看_天天av天天爽无码中文_中文字幕久久久久久久免费蜜桃麻豆_91中文字幕午夜福利亚洲天堂成人国产三级_欧美亚洲精品一级毛淫片_国产在线视频一区二区高清乱码99

【講座題目】 Semantic Association Mining

【講座嘉賓】 Prof .Ping Chen, University of Massachusetts Boston

【講座時間】7月21日（星期一）下午15：00-16：30

【講座地點】 bwin必贏唯一官網(wǎng)315教室

【摘要】  Discovery of risk factors affecting human health is very important. To medical researchers, these risk factors will provide valuable 

reasoning and modeling mechanism that are fundamentally important to medical and health research. In practice, health-related associations (risk factors) can provide basis for clinical decision making, health policy, and public guidance that directly impact 

health of individuals, families, communities, and populations. As the capability to capture and store medical data grows rapidly, 

the need for effective and efficient computation tools that facilitate such discoveries is high and increasing. This project aims to build an 

efficient medical association discovery system to extract significant, valid, non-redundant, and previously unknown associations 

of attributes (risk factors) from medical datasets. The goal and main innovations of this project are:

·         Integrating our knowledge-based approach with objective association mining method to generate only non-trivial, non-

redundant, valid, and previously unknown associations. These associations will serve as hypotheses and be further validated by biostatistic

 methods, which fundamentally changes current subjective formation of hypotheses to objective formation and discover radically different 

new knowledge; 

·         Building a research-grade medical association discovery system with a full suite of efficient and effective components: User Knowledge Acquisition 

Component, Semantic Network Building Component, Non-redundant Association Generation Component, Association Categorization 

Component, and Statistical Validation Component.