Browsing 08 Information and Computing Sciences by Title

DSpace/Manakin Repository

Search OPUS


Advanced Search

Browse

My Account

Browsing 08 Information and Computing Sciences by Title

Sort by: Order: Results:

  • Li, J; Robertson, T; Müller-Tomfelde, C (2012)
    This paper reports the findings from a field study of distributed scientific collaboration within a national animal health laboratory. Collaboration in this setting is challenged by the need for biosecurity - there are ...
  • Karol, A; Williams, M (Springer-Verlag Berlin, 2006-01)
    In a dynamic situation like robot soccer any individual player can only observe a limited portion of their environment at any given time. As such to develop strategies based upon planning and cooperation between different ...
  • Xiong, L; Libman, L; Mao, G (IEEE, 2009-01)
    We consider cooperative retransmission strategies in wireless networks, where the retransmission of a failed frame is handled not by the original source but rather by common neighbors overhearing the transmission. The ...
  • Si, S; Liu, W; Tao, D; Chan, KP (2011-01)
    Distribution calibration plays an important role in cross-domain learning. However, existing distribution distance metrics are not geodesic; therefore, they cannot measure the intrinsic distance between two distributions. ...
  • Qin, L; Yu, JX; Chang, L (ACM, 2012-01)
    Top-k query processing finds a list of k results that have largest scores w.r.t the user given query, with the assumption that all the k results are independent to each other. In practice, some of the top-k results returned ...
  • Zhou, T; Bian, W; Tao, D (IEEE, 2013-01)
    Abstract Nonnegative matrix factorization (NMF) becomes tractable in polynomial time with unique solution under separability assumption , which postulates all the data points are contained in the conical hull of a few ...
  • Dyson, LE (ED-MEDIA, 2011-01)
    In order to understand what mobile learning (mLearning) can contribute to the educational experience and learning outcomes of university students, an examination is made of three mLearning applications - lecture podcasting, ...
  • Wong, Y; Wilson, DN (ACM/Computer Science Press, 2004-01)
  • Duan, L; Duan, L; Tsang, I; Tsang, I; Xu, D; Xu, D; Chua, T; Chua, T (OmnipressOmnipress, 2009-01)
    We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for label prediction of patterns from the target ...
  • Pan, SJ; Tsang, I; Kwok, JT; Yang, Q (AAAI, 2009-01)
  • Pan, SJ; Tsang, I; Kwok, J; Yang, Q (IEEE, 2011-01)
    Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first ...
  • Cao, L; Zhang, C (2006)
    Actionable knowledge discovery Is one of Grand Challenges in KDD. To this end, many methodologies have been developed. However, they either view data mining as an autonomous data-driven trial-and-error process, or only ...
  • Cao, L; Zhang, C (IGI Global, 2008-01)
    Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support ...
  • Cao, L; Yu, PS; Zhang, C; Zhao, Y (Springer US, 2010)
    In the present thriving global economy a need has evolved for complex data analysis to enhance an organization's production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the ...
  • Cao, L; Zhang, C (Idea Publishing Group, 2006-01)
    Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, ...
  • Cao, L (IEEE, 2010-01)
    Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen ...
  • Cao, L (IEEE Computer Society, 2008-01)
    In deploying data mining into the real-world business, we have to cater for business scenarios, organizational factors, user preferences and business needs. However, the current data mining algorithms and tools often stop ...
  • Cao, L; Schurmann, R; Zhang, C (2005)
    Traditional data mining is a data-driven trial-and-error process. The patterns discovered via predefined models in the above process are generic patterns. Generally, they are often not really interesting to constraint-based ...