By Roger Lee (eds.)
This edited ebook provides medical result of the 13th IEEE/ACIS overseas convention on computing device and knowledge technology (ICIS 2014) which used to be hung on June 4-6, 2014 in Taiyuan, China. the purpose of this convention was once to assemble researchers and scientists, businessmen and marketers, academics, engineers, computing device clients, and scholars to debate the various fields of desktop technological know-how and to proportion their studies and alternate new rules and knowledge in a significant manner. study effects approximately all points (theory, functions and instruments) of laptop and knowledge technological know-how, and to debate the sensible demanding situations encountered alongside the best way and the ideas followed to unravel them.
The convention organizers chosen the easiest papers from these papers permitted for presentation on the convention. The papers have been selected in keeping with evaluation ratings submitted by way of individuals of this system committee, and underwent extra rigorous rounds of overview. This ebook captures 14 of the conference’s so much promising papers, and we impatiently watch for the $64000 contributions that we all know those authors will convey to the sector of machine and data science.
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Each classifier k extracts its own feature set X k = {x kj | j = 1, . . N } and generates confidence values, Y k = {yk j | j = 1, . . N }. The K feature sets are then concatenated to generate one global descriptor, χ = K k χ = {x j = [x 1j , . . , x Kj | j = 1, . . , N ]}. The original Context Extraction for k=1 Local Fusion algorithm [9] minimizes N C J= j=1 i=1 N K q vik di2jk u mji s=1 + βi u mji j=1 i=1 2 K C ωik yk j − t j , (1) k=1 C K subject to i=1 u ji = 1 ∀ j, u ji ∈ [0, 1] ∀i, j, k=1 vik = 1 ∀i, vik ∈ [0, 1] ∀i, k, K and k=1 ωik = 1 ∀i.
319–322, Cagliari, Italy (2002) 15. : Confidence based multiple classifier fusion in speaker verification. Pattern Recognit. Lett. 29(7), 957–966 (2008) 16. : Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI Mag. 12(2), 34–51 (1991) 17. : Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990) 18. : Combination of multiple classifiers using local accuracy estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 405–410 (1997) 19.
An Approach Based on Topic Model Xiaobing Sun, Bin Li, Yun Li and Ying Chen Abstract Mining software historical repositories (SHR) has emerged as a research direction Sun, over the past decade, which achieved substantial success in both research and practice to support various software maintenance tasks. Use of different types of SHR, or even different versions of the software project may derive different results for the same technique or approach of a maintenance task. Inclusion of unrelated information in SHR-based technique may lead to decreased effectiveness or even wrong results.