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Yi-Ping Phoebe Chen
Researcher at La Trobe University
Publications - 287
Citations - 5400
Yi-Ping Phoebe Chen is an academic researcher from La Trobe University. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 33, co-authored 268 publications receiving 4206 citations. Previous affiliations of Yi-Ping Phoebe Chen include Fujian Agriculture and Forestry University & Deakin University.
Papers
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Proceedings ArticleDOI
Concept Learning of Text Documents
Jiyuan An,Yi-Ping Phoebe Chen +1 more
TL;DR: This work introduces pruning power technique and proposes a robust enumeration-based concept learning algorithm that has more comprehensible and simplicity than by other methods.
Journal ArticleDOI
Using object and trajectory analysis to facilitate indexing and retrieval of video
Carlos Lopez,Yi-Ping Phoebe Chen +1 more
TL;DR: This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video.
Journal ArticleDOI
Exploiting multi-layered information to iteratively predict protein functions
TL;DR: The iterative approach takes into account the mutual and dynamic features of protein interactions when predicting functions, and addresses the issues of protein similarity measurement and prediction domain selection by introducing into the prediction algorithm a new semantic protein similarity and a method of selecting the multi-layer prediction domain.
Journal ArticleDOI
Knowledge Creation in Export Trading
TL;DR: The findings indicate that the knowledge management processes in export firms allow for the creation of new knowledge, which forms a basis for innovations and competitive intelligence.
Journal ArticleDOI
Using Dead Ants to improve the robustness and adaptability of AntNet routing algorithm
TL;DR: Two complementary strategies to improve AntNet routing algorithm's adaptability and robustness particularly under unpredicted traffic conditions such as network failure or sudden burst of network traffic are introduced.