Advances in Swarm Intelligence: Third International by Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser

By Seyed Naser Razavi, Nicolas Gaud, Abderrafiâa Koukam, Naser Mozayani (auth.), Ying Tan, Yuhui Shi, Zhen Ji (eds.)

This booklet and its spouse quantity, LNCS vols. 7331 and 7332, represent the lawsuits of the 3rd foreign convention on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The a hundred forty five complete papers awarded have been rigorously reviewed and chosen from 247 submissions. The papers are geared up in 27 cohesive sections overlaying all significant subject matters of swarm intelligence learn and developments.

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Only choose the right algorithm in different user scenario can obtain the best recommendation result. So that is the aim we propose an expandable recommendation system. 3 System Framework IPTV provide multiple interactive services. When user just login, the user might to select one video he or she interested to watch. In this scenario, system needs to recommend according to user’s overall interest. And when user is watching a video, An Expandable Recommendation System on IPTV 35 he or she might want to watch related videos.

017/2010/A2) of Macau Special Administrative Region of the People's Republic of China. References 1. : Regression models and life-tables. J. R. Statist. Soc. B 34, 187–220 (1972) 2. : Partial likelihood. Biometrika 62, 269–762 (1975) 3. : The lasso method for variable selection in the Cox model. Statist. Med. 16, 385–395 (1997) 4. : Variable selection for Cox’s proportional hazards model and frailty model. Ann. Statist. 30, 74–99 (2002) 5. : Adaptive Lasso for Cox’s proportional hazards model. Biometrika 94(3), 691–703 (2007) 6.

For each correct word phones, we use the PLPP as the pronunciation quality measurement: P LP P (P h) = P (P h|O) 1 = log P (q|O(q) ) τ p(O(q) |q) 1 = log τ p(O(q) |p) p∈Q (1) 20 J. Zhang, F. Pan, and Y. Yan Speech Cut Silence Formant Extract PLP Extract CDF Matching ASR Engine Scoring Features Decoding Network DuraƟon Model Final Score SVM Regression Fig. 1. The Testing Workflow where O is the forced-aligned observation sequence of the phone P h; τ is the number of frames in the acoustic segment O(q) ; Q is the set of phones and q is a phone; P (P h|O) is the phone log-posterior probability.

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