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Pet Shop Recommendation System based on Implicit Feedback
최희열,강윤희,강명주 한국디지털콘텐츠학회 2017 한국디지털콘텐츠학회논문지 Vol.18 No.8
Due to the advances in machine learning and artificial intelligence technologies, many new services have become available. Among such services, recommendation systems have already been successfully applied to commercial services and made profits as in online shopping malls. Most recommendation algorithms in commercial services are based on content analysis or explicit feedback rates as in movie recommendations. However, many online shopping malls have difficulties in content analysis or are lacking explicit feedbacks on their items, which results in no recommendation system for their items. Even for such service systems, user log data is easily available, and if recommendations are possible with such log data, the quality of their service can be improved. In this paper, we extract implicit feedback like click information for items from log data and provide a recommendation system based on the implicit feedback. The proposed system is applied to a real in-service online shopping mall.
전미정(Mi-Jeong Jun),이지훈(Ji-Hoon Lee),양희성(Hui-Sung Yang) 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.7
본 논문에서는 딥러닝 모델을 적용한 지중케이블 부분방전 통합 진단시스템을 제안한다. 실시간 결함데이터 및 학습 데이터를 위한 빅데이터 클러스터 구축, 부분방전 진단을 위한 딥러닝 결함진단 모델 구성 및 학습, 인공지능 모델 관리프로그램 개발 및 통합 진단관리시스템 구축을 수행하였다.
2차 Nonstationary 신호 분리 : 자연기울기 학습
최희열(Hee-Youl Choi),최승진(Seungjin Choi) 한국정보과학회 2002 한국정보과학회 학술발표논문집 Vol.29 No.1B
Most of source separation methods focus on stationary sources so higher-order statistics is necessary. In this paper we consider a problem of source separation when sources are second-order nonstationary stochastic processes. We employ the natural gradient method and develop learning algorithms for both linear feedback and feedforward neural networks. Thus our algorithms possess equivariant property. Local stability analysis shows that separating solutions are always locally stable stationary points of the proposed algorithms, regardless of probability distributions of sources.
Reference를 갖는 ICA를 이용한 자동적 P300 검출
최희열(Heeyoul Choi),최승진(Seungjin Choi) 한국정보과학회 2003 한국정보과학회 학술발표논문집 Vol.30 No.1B
The analysis of EEG data is an important task in the domain of Brain Computer Interface (BCI). In general, this task is extremely difficult because EEG data is very noisy and contains many artifacts and consists of mixtures of several brain waves. The P300 component of the evoked potential is a relatively evident signal which has a large positive wave that occurs around 300 msec after a task-relevant stimulus. Thus automatic detection of P300 is useful in BCI. To this end, in this paper we employ a method of reference-based independent component analysis (ICA) which overcomes the ordering ambiguity in the conventional ICA. We show here that ICA incorporating with prior knowledge is useful in the task of automatic P300 detection.
최희열(Heeyoul Choi),김숙정(Sookjeong Kim),최승진(Seungjin Choi) 한국정보과학회 2004 한국정보과학회 학술발표논문집 Vol.31 No.1B
A trust-region method is a quite attractive optimization technique. It is, in general, faster than the steepest descent method and is free of a learning rate unlike the gradient-based methods. In addition to its convergence property (between linear and quadratic convergence), its stability is always guaranteed, in contrast to the Newton's method. In this paper, we present an efficient implementation of the maximum likelihood independent component analysis (ICA) using the trustregion method, which leads to trust-region-based ICA (TR-ICA) algorithms. The useful behavior of our TR-ICA algorithms is confirmed through numerical experimental results.
최희열(Heeyoul Choi),최승진(Seungjin Choi) 한국정보과학회 2005 한국정보과학회 학술발표논문집 Vol.32 No.1
Isomap [1] is a manifold learning algorithm, which extends classical multidimensional scaling (MDS) by considering approximate geodesic distance instead of Euclidean distance. The approximate geodesic distance matrix can be interpreted as a kernel matrix, which implies that Isomap can be solved by a kernel eigenvalue problem. However, the geodesic distance kernel matrix is not guaranteed to be positive semidefinite. In this paper we employ a constant-adding method, which leads to the Mercer kernel-based Isomap algorithm. Numerical experimental results with noisy "Swiss roll" data, confirm the validity and high performance of our kernel Isomap algorithm.