By Bin Hu, Jiming Liu, Lin Chen, Ning Zhong
This publication constitutes the refereed lawsuits of the foreign convention on mind Informatics, BI 2011, held in Lanzhou, China, in September 2011. The 27 revised complete papers and six keynote talks have been rigorously reviewed and chosen for inclusion within the booklet. they're grouped in topcial sections on pondering and perception-centric investigations of human info processing structures; details applied sciences for the administration, research and use of mind information; cognition-inspired purposes. in addition, there's a part with eight papers from the workshop on meta-synthesis and intricate systems.
Read or Download Brain Informatics: International Conference, BI 2011, Lanzhou, China, September 7-9, 2011. Proceedings PDF
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Additional resources for Brain Informatics: International Conference, BI 2011, Lanzhou, China, September 7-9, 2011. Proceedings
Life-like Characters – Tools. Affective Functions and Applications. Springer, Heidelberg (2004) 9. : Exchanging Tacit Community Knowledge by Talkingvirtualized-egos. In: Proceedings of Agent 2000, pp. 285–292 (2000) 10. : Social Intelligence Design for Web Intelligence, Special Issue on Web Intelligence. IEEE Computer 35(11), 37–41 (2002) 11. : Producing Effective Shot Transitions in CG Contents based on a Cognitive model of User Involvement. IEICE Transactions of Information and Systems Special Issue of Life-like Agent and Its Communication, IEICE Trans.
In: BIBE, pp. F. Hsu et al. C. ): Combining Artificial Neural Nets: Ensemble and. Modular MultiNet Systems. Perspectives in Neural Computing. : Combinatorial Fusion for Improving Portfolio Performance. Advances in Social Science Research Using R, pp. 95–105. : Analysis of data fusion methods in virtual screening: Theoretical model. : Consensus scoring for improving enrichment in virtual screening. : Peculiarity oriented multidatabase mining. IEEE Trans. Knowl. Data Eng. ca The talk begins with a brief introduction to some of our research work in the past few years as well as the ongoing research.
One is applying this framework to creative learning and scientiﬁc discovery (both were thought as speciﬁc ill-structured problem solving tasks); the other is to elaborate this general framework with more detailed models in memory (such as chunk structure in short term memory) and the knowledge (and problem) representations, including the knowledge structure diﬀerence between experts and naives, diagrammatic representation and mental imagery. To meet the challenge of Web intelligence and to pioneer the eﬀective and eﬃcient ways of information processing at Web scale, as the ﬁrst step, we would learn this process from human brain, one of the greatest webs, based on Simon and Newell’s framework in problem solving.