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Parameter Identifiability in Statistical Machine Learning: A Review 期刊论文
NEURAL COMPUTATION, 2017, 卷号: 29, 期号: 5, 页码: 1151-1203
作者:  Ran, Zhi-Yong;  Hu, Bao-Gang
浏览  |  Adobe PDF(466Kb)  |  收藏  |  浏览/下载：348/108  |  提交时间：2017/07/18
Parameter Identifiability  Statistical Machine Learning  
Exploring Entrainment Patterns of Human Emotion in Social Media 期刊论文
PLOS ONE, 2016, 卷号: 11, 期号: 3, 页码: e0150630
作者:  He, Saike;  Zheng, Xiaolong;  Zeng, Daniel;  Luo, Chuan;  Zhang, Zhu
浏览  |  Adobe PDF(1152Kb)  |  收藏  |  浏览/下载：412/106  |  提交时间：2016/04/03
Enhanced HMAX model with feedforward feature learning for multiclass categorization 期刊论文
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2015, 卷号: 9, 页码: 1-14
作者:  Li, Yinlin;  Wu, Wei;  Zhang, Bo;  Li, Fengfu
浏览  |  Adobe PDF(3664Kb)  |  收藏  |  浏览/下载：393/87  |  提交时间：2016/03/30
Hmax  Biologically Inspired  Feedforward  Saliency Map  Middle Level Patch Learning  Feature Encoding  Multiclass Categorization  
Analyzing Positioning Strategies in Sponsored Search Auctions Under CTR-Based Quality Scoring 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2015, 卷号: 45, 期号: 4, 页码: 688-701
作者:  Yuan, Yong;  Zeng, Daniel;  Zhao, Huimin;  Li, Linjing
浏览  |  Adobe PDF(1156Kb)  |  收藏  |  浏览/下载：400/125  |  提交时间：2015/09/21
Terms-click-through Rate (Ctr)  Optimal Control  Polarization  Quality Score (Qs)  Sponsored Search  
Co-evolution-based mechanism design for sponsored search advertising 期刊论文
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2012, 卷号: 11, 期号: 6, 页码: 537-547
作者:  Yuan, Yong;  Zeng, Daniel
浏览  |  Adobe PDF(1718Kb)  |  收藏  |  浏览/下载：291/89  |  提交时间：2015/08/12
Sponsored Search Advertising  Mechanism Design  Co-evolutionary Simulation  Niche-based Evolution  
Dynamic dual adjustment of daily budgets and bids in sponsored search auctions 期刊论文
DECISION SUPPORT SYSTEMS, 2014, 卷号: 57, 期号: 0, 页码: 105-114
作者:  Zhang, Jie;  Yang, Yanwu;  Li, Xin;  Qin, Rui;  Zeng, Daniel
浏览  |  Adobe PDF(983Kb)  |  收藏  |  浏览/下载：354/99  |  提交时间：2015/08/12
Sponsored Search Auction  Budget Adjustment  Continuous Reinforcement Learning  Dynamic Adjustment