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10 results on '"Kim, Kijoo"'

1. A Site-Ranking Algorithm for a Small Group of Sites

Author

Kim, KiJoo, Kang, MunSu, Choi, YoungSik, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gervasi, Osvaldo, editor, and Gavrilova, Marina L., editor

Published
2007
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2. A Survey of Internship Programs for Management Undergraduates in AACSB-Accredited Institutions

Author

Kim, Eyong B., Kim, Kijoo, and Bzullak, Michael

Abstract

Purpose: The purpose of this paper is to survey the current status of internship programs for Management undergraduate students and to introduce a well-established internship program. Design/methodology/approach: A web page analysis was conducted on 473 institutions that have AACSB (the Association to Advance Collegiate Schools of Business) accreditation in the USA. The list of these institutions is from the AACSB web site (member school list) as of July 2010. A description of a well-established internship program is provided. The 15 item questionnaire is developed to get the students feedback on the required internship course described in this paper. Based on that survey and web search findings, suggestions for internship course improvement are provided. Findings: Most schools (96.5 per cent) offer some type of internship course but only a few schools (4.5 per cent) require students take an internship course. The pass/no pass grading system was preferred by a majority of universities (85 per cent). Students need to work an average of 169 hours for three credit internship courses. The most popular prerequisites are: GPA of 2.5 or higher; permission from an advisor or coordinator (62.5 per cent); and various other restrictions such as school minimum accumulated credit hours (22.5 per cent) and specific courses (33 per cent). The well-established program introduced has dedicated advisors to supervise the required internship courses. Practical implications: Internship program advisors/coordinators can assess the compatibility of their internship program with the introduced program. In addition, universities can benchmark against the introduced internship program to improve their current programs or establish a new program. Originality/value: If any universities want to improve their current internship courses, or establish an internship program, the paper's findings offer some guidelines. (Contains 5 tables and 1 figure.)

Published
2012
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3. A Focused Crawling for the Web Resource Discovery Using a Modified Proximal Support Vector Machines

Author

Choi, YoungSik, Kim, KiJoo, Kang, MunSu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gervasi, Osvaldo, editor, Gavrilova, Marina L., editor, Kumar, Vipin, editor, Laganà, Antonio, editor, Lee, Heow Pueh, editor, Mun, Youngsong, editor, Taniar, David, editor, and Tan, Chih Jeng Kenneth, editor

Published
2005
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4. Video Summarization Using Fuzzy One-Class Support Vector Machine

Author

Choi, YoungSik, Kim, KiJoo, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Laganá, Antonio, editor, Gavrilova, Marina L., editor, Kumar, Vipin, editor, Mun, Youngsong, editor, Tan, C. J. Kenneth, editor, and Gervasi, Osvaldo, editor

Published
2004
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10. A Focused Crawling for the Web Resource Discovery Using a Modified Proximal Support Vector Machines.

Author

Gervasi, Osvaldo, Gavrilova, Marina L., Kumar, Vipin, Laganà, Antonio, Lee, Heow Pueh, Mun, Youngsong, Taniar, David, Tan, Chih Jeng Kenneth, Choi, YoungSik, Kim, KiJoo, and Kang, MunSu

Abstract

With the rapid growth of the World Wide Web, a focused crawling has been increasingly of importance. The goal of the focused crawling is to seek out and collect the pages that are relevant to a predefined set of topics. The determination of the relevance of a page to a specific topic has been addressed as a classification problem. However, when training the classifiers, one can often encounter some difficulties in selecting negative samples. Such difficulties come from the fact that collecting a set of pages relevant to a specific topic is not a classification process by nature. In this paper, we propose a novel focused crawling method using only positive samples to represent a given topic as a form of hyperplane, where we can obtain such representation from a modified Proximal Support Vector Machines. The distance from a page to the hyperplane is used to prioritize the visit order of the page. We demonstrated the performance of the proposed method over the WebKB data set and the Web. The promising results suggest that our proposed method be more effective to the focused crawling problem than the traditional approaches. [ABSTRACT FROM AUTHOR]

Published
2005
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