freepeople性欧美熟妇, 色戒完整版无删减158分钟hd, 无码精品国产vα在线观看DVD, 丰满少妇伦精品无码专区在线观看,艾栗栗与纹身男宾馆3p50分钟,国产AV片在线观看,黑人与美女高潮,18岁女RAPPERDISSSUBS,国产手机在机看影片

正文內(nèi)容

電子信息科學(xué)與技術(shù)畢業(yè)論文-數(shù)據(jù)流中概念漂移檢測與分類方法研究-資料下載頁

2025-06-07 00:04本頁面
  

【正文】 cting nonstationary changes[J]. IEEE Transactions on Neural Networks, 2021, 19(7): 11451153. [70]Alippi C, Roveri M. Justintime adaptive classifiers—Part II: Designing the classifier[J]. IEEE Transactions on Neural Networks, 2021, 19(12): 20532064. [71]Alippi C, Boracchi G, and Roveri M. An effective justintime adaptive classifier for gradual concept drifts[C]//Proceedings of the 2021 International Joint Conference on Neural Networks. San Jose, USA, 2021: 16751681. [72]Peter V and Abranham B. Entropybased concept drift detection[C]//Proceedings of the 6th International Conference on Data Mining. Hong Kong, China, 2021: 11131118. [73]Kuncheva L. Change detection in streaming multivariate data using likelihood detectors[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, (to appear) . [74]Kubat M. To load balancing in puter works[J]. International Journal of Cyberics and System, 1992, 23(34): 389400. [75]Cohen L, AvrahamiBakish G, and Last M, etal. Realtime data mining of nonstationary data streams from sensor works[J]. Information Fusion, 2021,9(3): 344353. 22 [76]Harries M, Horn K. Detecting concept drift in financial time series prediction using symbolic machine learning[C]//Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence. Canberra, Australia, 1995: 9198. [77]Movellan J R, Mineiro P. Robust sensor fusion: Analysis and application to audio visual speech recognition[J]. Machine Learning, 1998, 32(2): 85100. [78]Bernstein A, Vorburger P, and Egger P. A scenariobased approach for direct interruptability prediction on wearable devices[J]. International Journal of Pervasive Computing and Communications, 2021, 3(4): 426438. [79]Gao J, Ding B, and Fan W, etal. Classifying data stream with skewed class distributions and concept drifts[J]. IEEE on Inter Computing, 2021, 12(6): 3749. [80]Chen S, He H. SERA: selectively recursive approach towards nonstationary imbalanced stream data mining[C]// Proceedings of the 2021 International Joint Conference on Neural Networks. Atlanta, Geian, USA, 2021: 522529. [81]Chen S, He H, and Li K, etal. MuSeRA: multiple selectively recursive approach towards nonstationary imbalanced stream data mining[C]//Proceedings of the 2021 International Joint Conference on Neural Networks. Barcelona, Spain, 2021: 18. [82]Chen S, He H. Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach[J]. Evolving Systems, 2021, 2(1):3550. [83]Lichtenwalter R N, Chawla N V. Adaptive methods for classification in arbitrarily imbalanced and drifting data streams[C]//Proceedings of the 13th PacificAsia Conference on Knowledge Discovery and Data Mining. Bangkok ,Thailand, 2021: 5375. [84]Gregory D, Robi P. An ensemble based incremental learning framework for concept drift and class imbalance[C]//Proceedings of the 2021 International Joint Conference on Neural Networks. Barcelona, Spain, 2021: 917. [85]Zhang J, Hu X, Zhang Y, and Li P. An Efficient Ensemble Method for Classifying Skewed Data Streams[C]// Proceedings of the 7th International Conference on Intelligent Computing. Zhengzhou, China, 2021: 144151. 23 [86]Ramamurthy S, Bhatnagar R. Tracking recurrent concept drift in streaming data using ensemble classifiers[C]// Proceedings of the 6th International Conference on Machine Learning and Applications. Cincinnati, OH, USA, 2021: 404409. [87]Li P P, Wu X D, and Hu X G. Mining Recurring Concept Drifts with Limited Labeled Streaming Data[C]// Proceedings of the 2th Asian Conference on Machine Learning. Tokyo, Japan, 2021: 241252. [88]Masud M M, AlKhateeb T M, and Khan L, etal. Detecting Recurring and Novel Classes in ConceptDrifting Data Streams[C]//Proceedings of the 2021 International Conference on Data Mining. Vancouver, Canada, 2021: 11761181. [89]Xue J C, Weiss G M. Quantification and semisupervised classification methods for handling changes in class distribution[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, French, 2021: 897906. [90]Zhang P, Zhu X, and Guo L. Mining data streams with labeled and unlabeled training examples[C]//Proceedings of the ninth IEEE International Conference on Data Mining. Miami, FL,USA ,2021: 627636. [91]Li P, Wu X D, and Hu X G. Learning from concept drift data streams with unlabeled data[C]//Proceedings of the 24th AAAI Conference on Artificial Intelligence. Atlanta, Geian USA, 2021: 19451946. [92]Fan W, Huang Y, and Wang H, et al. Active mining of data streams[C]//Proceedings of the 4th SIAM International Conference on Data Mining. Lake Buena Vista, Florida, USA, 2021: 457461. [93]Zhu X, Zhang P, and Lin X. Active learning from stream data using optimal weight classifier ensemble[J]. IEEE Transactions on Systems, Man, and Cyberics, Part B: Cyberics, 2021, 40(6): 16071621. [94]Masud M, Gao J, and Khan L, et al. Classification and novel class detection in data streams with active mining[C]//Proceedings of the 14th PacificAsia Conference on Knowledge Discovery and Data Mining. Hyderabad, India, 2021: 311324. 24 [95]Zliobaite I, Bifet A, and Pfahringer B, etal. Active learning with evolving streaming data[C]//Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Athens, Greece, 2021,11: 597612. [96]Chu W, Zinkevich M, and Li L, etal. Unbiased online active learning in data streams[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, USA, 2021: 195203. [97]Kholghi M, Keyvanpour M R. Active Learning Framework Combining SemiSupervised Approach for Data Stream Mining[J]. Intelligent Computing and Information Science, 2021, 135 :238243.
點擊復(fù)制文檔內(nèi)容
畢業(yè)設(shè)計相關(guān)推薦
文庫吧 www.dybbs8.com
備案圖鄂ICP備17016276號-1