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集成学习 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "集成学习" in Chinese language version.

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34doi.org
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  • Amini, Shahram M.; Parmeter, Christopher F. Bayesian model averaging in R (PDF). Journal of Economic and Social Measurement. 2011, 36 (4): 253–287 [2019-01-01]. （原始内容存档 (PDF)于2017-12-03）. 

doi.org

  • Opitz, D.; Maclin, R. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research. 1999, 11: 169–198. doi:10.1613/jair.614. 
  • Polikar, R. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine. 2006, 6 (3): 21–45. doi:10.1109/MCAS.2006.1688199. 
  • Rokach, L. Ensemble-based classifiers. Artificial Intelligence Review. 2010, 33 (1-2): 1–39. doi:10.1007/s10462-009-9124-7. 
  • Hoeting, J. A.; Madigan, D.; Raftery, A. E.; Volinsky, C. T. Bayesian Model Averaging: A Tutorial. Statistical Science. 1999, 14 (4): 382–401. JSTOR 2676803. doi:10.2307/2676803. 
  • Castillo, I.; Schmidt-Hieber, J.; van der Vaart, A. Bayesian linear regression with sparse priors. Annals of Statistics. 2015, 43 (5): 1986–2018. arXiv:1403.0735 可免费查阅. doi:10.1214/15-AOS1334. 
  • Breiman, L., Stacked Regression, Machine Learning, 24, 1996 doi:10.1007/BF00117832
  • Woźniak, Michał; Graña, Manuel; Corchado, Emilio. A survey of multiple classifier systems as hybrid systems. Information Fusion. March 2014, 16: 3–17. doi:10.1016/j.inffus.2013.04.006. 
  • Rodriguez-Galiano, V.F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing. January 2012, 67: 93–104. Bibcode:2012JPRS...67...93R. doi:10.1016/j.isprsjprs.2011.11.002. 
  • Giacinto, Giorgio; Roli, Fabio. Design of effective neural network ensembles for image classification purposes. Image and Vision Computing. August 2001, 19 (9-10): 699–707. doi:10.1016/S0262-8856(01)00045-2. 
  • Xia, Junshi; Yokoya, Naoto; Iwasaki, Yakira. A novel ensemble classifier of hyperspectral and LiDAR data using morphological features. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). March 2017: 6185–6189. doi:10.1109/ICASSP.2017.7953345. 
  • Giacinto, G.; Roli, F.; Fumera, G. Design of effective multiple classifier systems by clustering of classifiers. Proceedings 15th International Conference on Pattern Recognition. ICPR-2000. September 2000: 160-163. doi:10.1109/ICPR.2000.906039. 
  • Du, Peijun; Liu, Sicong; Xia, Junshi; Zhao, Yindi. Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion. January 2013, 14 (1): 19–27. doi:10.1016/j.inffus.2012.05.003. 
  • Bruzzone, Lorenzo; Cossu, Roberto; Vernazza, Gianni. Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images. Information Fusion. December 2002, 3 (4): 289–297. doi:10.1016/S1566-2535(02)00091-X. 
  • Raj Kumar, P. Arun; Selvakumar, S. Distributed denial of service attack detection using an ensemble of neural classifier. Computer Communications. July 2011, 34 (11): 1328–1341. doi:10.1016/j.comcom.2011.01.012. 
  • Shabtai, Asaf; Moskovitch, Robert; Elovici, Yuval; Glezer, Chanan. Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey. Information Security Technical Report. February 2009, 14 (1): 16–29. doi:10.1016/j.istr.2009.03.003. 
  • Zhang, Boyun; Yin, Jianping; Hao, Jingbo; Zhang, Dingxing; Wang, Shulin. Malicious Codes Detection Based on Ensemble Learning. Autonomic and Trusted Computing. 2007: 468–477. doi:10.1007/978-3-540-73547-2_48. 
  • Menahem, Eitan; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval. Improving malware detection by applying multi-inducer ensemble. Computational Statistics & Data Analysis. February 2009, 53 (4): 1483–1494. doi:10.1016/j.csda.2008.10.015. 
  • Locasto, Michael E.; Wang, Ke; Keromytis, Angeles D.; Salvatore, J. Stolfo. FLIPS: Hybrid Adaptive Intrusion Prevention. Recent Advances in Intrusion Detection. 2005: 82–101. doi:10.1007/11663812_5. 
  • Giacinto, Giorgio; Perdisci, Roberto; Del Rio, Mauro; Roli, Fabio. Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Information Fusion. January 2008, 9 (1): 69–82. doi:10.1016/j.inffus.2006.10.002. 
  • Mu, Xiaoyan; Lu, Jiangfeng; Watta, Paul; Hassoun, Mohamad H. Weighted voting-based ensemble classifiers with application to human face recognition and voice recognition. 2009 International Joint Conference on Neural Networks. July 2009. doi:10.1109/IJCNN.2009.5178708. 
  • Yu, Su; Shan, Shiguang; Chen, Xilin; Gao, Wen. Hierarchical ensemble of Gabor Fisher classifier for face recognition. Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on Automatic Face and Gesture Recognition (FGR06). April 2006: 91–96. doi:10.1109/FGR.2006.64. 
  • Su, Y.; Shan, S.; Chen, X.; Gao, W. Patch-based gabor fisher classifier for face recognition. Proceedings - International Conference on Pattern Recognition. September 2006, 2: 528–531. doi:10.1109/ICPR.2006.917. 
  • Liu, Yang; Lin, Yongzheng; Chen, Yuehui. Ensemble Classification Based on ICA for Face Recognition. Proceedings - 1st International Congress on Image and Signal Processing, IEEE Conference, CISP 2008. July 2008: 144–148 [2019-01-01]. doi:10.1109/CISP.2008.581. （原始内容存档于2019-06-09）. 
  • Rieger, Steven A.; Muraleedharan, Rajani; Ramachandran, Ravi P. Speech based emotion recognition using spectral feature extraction and an ensemble of kNN classifiers. Proceedings of the 9th International Symposium on Chinese Spoken Language Processing, ISCSLP 2014. 2014: 589–593. doi:10.1109/ISCSLP.2014.6936711. 
  • Krajewski, Jarek; Batliner, Anton; Kessel, Silke. Comparing Multiple Classifiers for Speech-Based Detection of Self-Confidence - A Pilot Study. 2010 20th International Conference on Pattern Recognition. October 2010: 3716–3719. doi:10.1109/ICPR.2010.905. 
  • Rani, P. Ithaya; Muneeswaran, K. Recognize the facial emotion in video sequences using eye and mouth temporal Gabor features. Multimedia Tools and Applications. 25 May 2016, 76 (7): 10017–10040. doi:10.1007/s11042-016-3592-y. 
  • Rani, P. Ithaya; Muneeswaran, K. Facial Emotion Recognition Based on Eye and Mouth Regions. International Journal of Pattern Recognition and Artificial Intelligence. August 2016, 30 (07): 1655020. doi:10.1142/S021800141655020X. 
  • Rani, P. Ithaya; Muneeswaran, K. Emotion recognition based on facial components. Sādhanā. 28 March 2018, 43 (3). doi:10.1007/s12046-018-0801-6. 
  • Louzada, Francisco; Ara, Anderson. Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool. Expert Systems with Applications. October 2012, 39 (14): 11583–11592. doi:10.1016/j.eswa.2012.04.024. 
  • Sundarkumar, G. Ganesh; Ravi, Vadlamani. A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance. Engineering Applications of Artificial Intelligence. January 2015, 37: 368–377. doi:10.1016/j.engappai.2014.09.019. 
  • Kim, Yoonseong; Sohn, So Young. Stock fraud detection using peer group analysis. Expert Systems with Applications. August 2012, 39 (10): 8986–8992. doi:10.1016/j.eswa.2012.02.025. 
  • Savio, A.; García-Sebastián, M.T.; Chyzyk, D.; Hernandez, C.; Graña, M.; Sistiaga, A.; López de Munain, A.; Villanúa, J. Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI. Computers in Biology and Medicine. August 2011, 41 (8): 600–610. doi:10.1016/j.compbiomed.2011.05.010. 
  • Ayerdi, B.; Savio, A.; Graña, M. Meta-ensembles of classifiers for Alzheimer's disease detection using independent ROI features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). June 2013, (Part 2): 122–130. doi:10.1007/978-3-642-38622-0_13. 
  • Gu, Quan; Ding, Yong-Sheng; Zhang, Tong-Liang. An ensemble classifier based prediction of G-protein-coupled receptor classes in low homology. Neurocomputing. April 2015, 154: 110–118. doi:10.1016/j.neucom.2014.12.013. 

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jstor.org

mathworks.com

uk.mathworks.com

microsoft.com

research.microsoft.com

psu.edu

citeseerx.ist.psu.edu

r-project.org

cran.r-project.org

washington.edu

cs.washington.edu

web.archive.org