Hwanjo Yu
포항공과대학교 컴퓨터공학과 · 컴퓨터과학
Hwanjo Yu 교수의 연구실은 대량의 데이터와 개인정보 보호 문제를 고려한 지능형 데이터 분석 기법을 핵심으로 합니다. 특히, 대규모 데이터 환경에서 효율적으로 작동하는 서포트 벡터 머신 기반 알고리즘 개발과 개인정보 유출 위험을 최소화하는 분산 지식 발굴 기술에 주력하고 있습니다. 또한 의료 기록의 실재성과 보안을 동시에 확보하는 합성 데이터 생성 기법과, 희소한 양의 정상 데이터에서 유의미한 패턴을 추출하는 단일 클래스 분류 기법 등, 실제 응용에 초점을 맞춘 연구를 진행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convery several salient properties that other methods hardly provide. However, despite the prominent properties of SVMs, they are not as favored for large-scale data mining as for pattern recognition or machine learning because the training complexity of SVMs is highly dependent on the size of a data set. Many real-world data mining applicati
Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What we need is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the non-disclosure of data. Support vector machine classification is one of the most widely used classific
DAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.
Nowadays, high throughput experimental techniques make it feasible to examine and collect massive data at the molecular level. These data, typically mapped to a very high dimensional feature space, carry rich information about functionalities of certain chemical or biological entities and can be used to infer valuable knowledge for the purposes of classification and prediction. Typically, a small number of features or feature combinations may play determinant roles in functional discrimination.
Finding related articles from the PubMed (a large biomedical literature repository) is challenging because it is hard to express the user's specific relevance in the given query interface and a keyword query typically retrieves many results. Biomedical researchers spend a critical amount of time (e.g., often more than several days) in the literature search process. This paper proposes RefMed, a novel search system for PubMed, which supports relevance ranking by enabling relevance feedback on Pub
