inforience.net

Inforience

Inforience develops advanced technologies (detection, measurement, analysis, interpretation, learning) that can be applied to various data. Inforience conducts academic research and API development based on data analysis and machine learning technologies. (contact to: contact@inforience.com) Inforience 는 지능적인 데이터 활용 시스템을 연구개발합니다. 다양한 데이터에 적용할 수 있는 (탐지, 측정, 분석, 해석, 학습) 기술들을 개발하고, 데이터 분석과 머신러닝. Continue Reading →. CoDIP을 활용하여 1차원 시계열 데이터에 포함된 change point들을 자동으로 탐지하자.(1). March 12, 2018. March 12, 2018.

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Inforience | inforience.net Reviews

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Inforience develops advanced technologies (detection, measurement, analysis, interpretation, learning) that can be applied to various data. Inforience conducts academic research and API development based on data analysis and machine learning technologies. (contact to: contact@inforience.com) Inforience 는 지능적인 데이터 활용 시스템을 연구개발합니다. 다양한 데이터에 적용할 수 있는 (탐지, 측정, 분석, 해석, 학습) 기술들을 개발하고, 데이터 분석과 머신러닝. Continue Reading →. CoDIP을 활용하여 1차원 시계열 데이터에 포함된 change point들을 자동으로 탐지하자.(1). March 12, 2018. March 12, 2018.

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Inforience | inforience.net Reviews

https://inforience.net

Inforience develops advanced technologies (detection, measurement, analysis, interpretation, learning) that can be applied to various data. Inforience conducts academic research and API development based on data analysis and machine learning technologies. (contact to: contact@inforience.com) Inforience 는 지능적인 데이터 활용 시스템을 연구개발합니다. 다양한 데이터에 적용할 수 있는 (탐지, 측정, 분석, 해석, 학습) 기술들을 개발하고, 데이터 분석과 머신러닝. Continue Reading →. CoDIP을 활용하여 1차원 시계열 데이터에 포함된 change point들을 자동으로 탐지하자.(1). March 12, 2018. March 12, 2018.

INTERNAL PAGES

inforience.net

1

커피매장 방문고객 만족도 측정 및 영향요인 – Inforience

https://inforience.net/2014/07/27/9

커피매장 방문고객 만족도 측정 및 영향요인. 이 포스팅은 Inforience 에서 발표한 2013년 한국소비문화학회 춘계학술대회 ( http:/ www.kcca1997.org/bbs/data/com010 10 02.pdf. 논문 – “정지영, 하미경, 최진혁, 커피매장 방문고객 충성도 및 영향요인” 을 기반으로 구성되었습니다. 저는 사실 커피 맛에 대해서는 잘 모르는데, 사실은 혼자 시간 보내기 좋아서 집 앞의 에헤라디야 커피 에 자주 가요. 가까우니까 건너편 뚝섬 플레이스 에도 종종 가고요. 그림1 Starbuck Experience Map 고객이 커피 매장에 들어섰을 때부터 나갈 때 까지의 경험 내용을 묘사 ( 커피는 좋았는데, 음악이 좀 시끄러웠고, 공간도 좀 좁고, 전기 콘센트도 부족한 듯 ). 그림2 아무리 커피매장에 다른 목적으로 왔다고 해도 커플끼리 애정행각을 벌이는 것은 용서할 수 없다. 경찰에 신고할 거다. 앞으로 에헤라디야 커피점 에만 방문하겠습니다! 3 연구방법 및 모형.

2

July 2014 – Inforience

https://inforience.net/2014/07

커피매장 방문고객 만족도 측정 및 영향요인. July 27, 2014. October 6, 2015. 이 포스팅은 Inforience 에서 발표한 2013년 한국소비문화학회 춘계학술대회 ( http:/ www.kcca1997.org/bbs/data/com010 10 02.pdf. 논문 – “정지영, 하미경, 최진혁, 커피매장 방문고객 충성도 및 영향요인” 을 기반으로 구성되었습니다. 저는 사실 커피 … Continue reading 커피매장 방문고객 만족도 측정 및 영향요인. 데이터에서 가치를 추출하기 위해 떠나는 여행, 그 출발선에서 (데이터 탐험, 해석, 그리고 추론의 중요성). July 27, 2014. October 5, 2015. Create a free website or blog at WordPress.com. Create a free website or blog at WordPress.com. Follow “Inforience”.

3

[INFTN-14-001] A Framework for Finding High Interest Content Pages from Web Visit History at Client Side – Inforience

https://inforience.net/2014/09/28/inftn-14-001-a-framework-for-finding-high-interest-content-pages-from-web-visit-history-at-client-side

INFTN-14-001] A Framework for Finding High Interest Content Pages from Web Visit History at Client Side. 21 Web Usage Mining. 22 Client Side Web Usage Mining. 3 Research Goals and Approach. Our research consists of several phases. The first phase is a field study in which raw data are collected. The second phase is analysis of the collected raw data. In the second phase, we design our implicit indicators. The usefulness of the designed indicators is analyzed in the third phase. The fo...Figure 2. (a)...

4

Human&IT – Inforience

https://inforience.net/category/humanit

INFTN-14-001] A Framework for Finding High Interest Content Pages from Web Visit History at Client Side. September 28, 2014. October 5, 2015. 커피매장 방문고객 만족도 측정 및 영향요인. July 27, 2014. October 6, 2015. 이 포스팅은 Inforience 에서 발표한 2013년 한국소비문화학회 춘계학술대회 ( http:/ www.kcca1997.org/bbs/data/com010 10 02.pdf. 논문 – “정지영, 하미경, 최진혁, 커피매장 방문고객 충성도 및 영향요인” 을 기반으로 구성되었습니다. 저는 사실 커피 … Continue reading 커피매장 방문고객 만족도 측정 및 영향요인. Blog at WordPress.com. Create a free website or blog at WordPress.com.

5

About – Inforience

https://inforience.net/about

Leave a Reply Cancel reply. Enter your comment here. Please log in using one of these methods to post your comment:. Address never made public). You are commenting using your WordPress.com account. ( Log Out. You are commenting using your Twitter account. ( Log Out. You are commenting using your Facebook account. ( Log Out. You are commenting using your Google account. ( Log Out. Notify me of new comments via email. Notify me of new posts via email. 커피매장 방문고객 만족도 측정 및 영향요인. Blog at WordPress.com.

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Inforience develops advanced technologies (detection, measurement, analysis, interpretation, learning) that can be applied to various data. Inforience conducts academic research and API development based on data analysis and machine learning technologies. (contact to: contact@inforience.com) Inforience 는 지능적인 데이터 활용 시스템을 연구개발합니다. 다양한 데이터에 적용할 수 있는 (탐지, 측정, 분석, 해석, 학습) 기술들을 개발하고, 데이터 분석과 머신러닝. Continue Reading →. CoDIP을 활용하여 1차원 시계열 데이터에 포함된 change point들을 자동으로 탐지하자.(1). March 12, 2018. March 12, 2018.

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