提醒自己少走弯路的十条忠告

[1] 葛彤 基于机器学习的室内wifi定位算法研究 北京邮电2017 [2] 卞智. 基于机器学习算法的指纹匹配定位技术研究[D]. 北京邮电大学, 2017. [3] 覃玉清. 基于深度学习的WIFI定位算法[D]. 南京大学, 2014. [4] 刘万寿. 基于WiFi技术的室内无线定位方法研究[D]. 哈尔滨工程大学, 2015. [5] 王鹏. 基于机器学习的无线传感网络节点定位方法研究[D]. 浙江工业大学, 2011. [6] 李军, 何星, 蔡云泽,等. 基于K-means和Random Forest的WiFi室内定位方法[J]. 控制工程, 2017, 24(4):787-792. [7] 徐龙阳. 基于机器学习的室内定位方法综述[J]. 电脑知识与技术, 2018(1). [8] Cong C, Men X. An Innovative Indoor Location Algorithm based on Supervised Learning and WIFI Fingerprint Classification[C]// International Conference On Signal And Information Processing, Networking And Computers. Springer, Singapore, 2017:238-246. [9] Elbasiony R, Gomaa W. WiFi Localization for Mobile Robots based on Random Forests and GPLVM[C]// International Conference on Machine Learning and Applications. IEEE, 2015:225-230. [10] Hernández N, Ocaña M, Alonso J M, et al. WiFi-based indoor localization and tracking of a moving device[C]// Ubiquitous Positioning, Indoor Navigation & Location based Services. IEEE, 2014:281-289. [11] Wang ， Qiaojun Kernel learning and applications in wireless localization [12] Wu H, Chen J, Wang C, et al. A Kernel-based Localization Approach in Wireless Sensor Networks[C]// International Conference on Future Generation Communication and [13] Tran D A, Nguyen T. Localization In Wireless Sensor Networks based on Support Vector Machines[J]. IEEE Transactions on Parallel & Distributed Systems, 2008, 19(7):981-994.NETWORKING. IEEE, 2008:31-34. [14] Jaroenkittichai P, Leelarasmee E. Utilizing Multiple Data Sources for Localization in Wireless Sensor Networks based on Support Vector Machines[J]. Ieice Transactions on Fundamentals of Electronics Communications & Computer Sciences, 2013, E96.A(11):2081-2088. [15] Zhu F, Wei J. Localization Algorithm in Wireless Sensor Networks based on Improved Support Vector Machine[J]. Journal of Nanoelectronics & Optoelectronics, 2016, 12(5):452-459. [16] Salamah A H, Tamazin M, Sharkas M A, et al. An enhanced WiFi indoor localization system based on machine learning[C]// International Conference on Indoor Positioning and Indoor Navigation. IEEE, 2016. [17] Zhao J, Wang J. WiFi indoor positioning algorithm based on machine learning[C]// IEEE International Conference on Electronics Information and Emergency Communication. IEEE, 2017:279-283. [18] Zhao J, Wang J. WiFi indoor positioning algorithm based on machine learning[C]// IEEE International Conference on Electronics Information and Emergency Communication. IEEE, 2017:279-283. [19] Pan J J, Yang Q, Pan S J. online co-localization in indoor wireless networks by dimension reduction[C]// National Conference on Artificial Intelligence. AAAI Press, 2007:1102-1107. [20] Pan J J, Yang Q, Chang H, et al. A manifold regularization approach to calibration reduction for sensor-network based tracking[C]// National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, Usa. DBLP, 2006:988–993. [21] Laine S, Aila T. Temporal Ensembling for Semi-Supervised Learning[J]. 2016. [22] Xiaojin Z. Semi-Supervised Learning Literature Sur-vey[J]. 2005, 37(1):63-77. [23] 黄涛涛, 顾晶晶, 庄毅. 基于半监督拉普拉斯映射的移动定位算法[J]. 计算机工程, 2018, 44(1):144-148. [24] 李昱. 半监督流形学习算法研究和应用[D]. 西安电子科技大学, 2010. [25] 刘海红, 周聪辉. 半监督拉普拉斯特征映射算法[J]. 计算机工程与设计, 2012, 33(2):601-606. [26] 杨剑, 王珏, 钟宁. 流形上的Laplacian半监督回归[J]. 计算机研究与发展, 2007, 44(7):1121-1127. [27] Yang B, Xu J, Yang J, et al. Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application[J]. Cluster Computing, 2010, 13(4):435-446. [28] Zhou M, Tang Y, Nie W, et al. GrassMA: Graph-based Semi-supervised Manifold Alignment for Indoor WLAN Localization[J]. IEEE Sensors Journal, 2017, PP(99):1-1. [29] Belkin M, Niyogi P, Sindhwani V. Manifold Regularization: A Geometric framework for Learning from Labeled and Unlabeled Examples[M]. JMLR.org, 2006. [30] Wang J, Luo J, Pan S J, et al. Learning-based Outdoor Localization Exploiting Crowd-Labeled WiFi Hotspots[J]. IEEE Transactions on Mobile Computing, PP(99):1-1. [31] Pan J J, Yang Q, Pan S J. online co-localization in indoor wireless networks by dimension reduction[C]// National Conference on Artificial Intelligence. AAAI Press, 2007:1102-1107. [1] Wang J, Tan N, Luo J, et al. WOLoc: WiFi-only outdoor localization using crowdsensed hotspot labels[C]// INFOCOM 2017 - IEEE Conference on Computer Communications, IEEE. IEEE, 2017. [2] Wang J, Luo J, Pan S J, et al. Learning-based Outdoor Localization Exploiting Crowd-Labeled WiFi Hotspots[J]. IEEE Transactions on Mobile Computing, PP(99):1-1. [3] Belkin M. Semi-supervised learning on manifolds[J]. Machine Learning, 2004, 56(1-3):209-239. [4] Zheng V W, Pan S J, Yang Q, et al. Transferring multi-device localization models using latent multi-task learning[C]// National Conference on Artificial Intelligence. AAAI Press, 2008:1427-1432. [5] Pan R, Zhao J, Zheng V W, et al. Domain-constrained semi-supervised mining of tracking models in sensor networks[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, Usa, August. DBLP, 2007:1023-1027. [6] Pan J J, Yang Q, Chang H, et al. A manifold regularization approach to calibration reduction for sensor-network based tracking[C]// National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, July 16-20, 2006, Boston, Massachusetts, Usa. DBLP, 2006:988–993. [7] Pan J J, Pan S J, Zheng V W, et al. Digital Wall: A Power-efficient Solution for Location-based Data Sharing[C]// IEEE International Conference on Pervasive Computing & Communications. IEEE Computer Society, 2008:645-650. [8] Pan S J, Kwok J T, Yang Q, et al. Adaptive localization in a dynamic WiFi environment through multi-view learning[C]// National Conference on Artificial Intelligence. AAAI Press, 2007:1108-1113. [9] Belkin M, Niyogi P. Semi-Supervised Learning on Riemannian Manifolds[J]. Machine Learning, 2004, 56(1-3):209-239. [10] Pan J J, Pan S J, Yin J, et al. Tracking mobile users in wireless networks via semi-supervised colocalization[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(3):587.