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大数据下的机器学习算法综述_何清 评分

随着产业界数据量的爆炸式增长,大数据概念受到越来越多的关注. 由于大数据的海量、复杂多样、变化 快的特性,对于大数据环境下的应用问题,传统的在小数据上的机器学习算法很多已不再适用. 因此,研究大数据 环境下的机器学习算法成为学术界和产业界共同关注的话题. 文中主要分析和总结当前用于处理大数据的机器学 习算法的研究现状. 此外,并行是处理大数据的主流方法,因此介绍一些并行算法,并引出大数据环境下机器学习 研究所面临的问题. 最后指出大数据机器学习的研究趋势.
ordan Little bootstraps Boot frap ordan 4 4.1 4.2 Kol- Tucker Memory -Efficient Tucker Decomposition MET MET densed Nearest Neighbor CNN R duced nearest neighbor RnN Ed MET ted Nearest Neighbor ENN Wahba h 10 Regularized CNN Kernel estimation RKE Robust Fast Cnn FCnn Manifold Unfolding RMU ordan Boot strap Ie 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net Self-organizing Map SOM SOM 16 Fast SoM fSom 1g sⅤD、 RP PCA Fuzzy Lower- pproximation -Based Fuzzy Rough Set Feature Se lection with Threshold T-FRFS Quickreduct L - FRFS Quevedo 16 VM Simulated annealing and Genetic algo- hm saga NP askov SVM Pal SVM Minimax probability Machine ⅥPM M Incremental Kernel pca u Il Least Squares SVM Ls-svM Kim 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net C Q-training Based Decision tree on random forest Co -forest franco -Arcega n Yang Parallel Averaging Ir ncre Stochastic gradient Descent AsgD 120 mentally Optimized Very Fast Decision Tree 、l000 iOVFDT Information bot Benaim Extreme Learning Machine elm lg ELM 上CM ELM Havens ELMI ELM ELM FCM ELM ELM FCM Random Sampling Plus Extension FCM ELM FCM Bit-Reduced FCm M A ELM proximate Kernel FCM FCM Havens FCM k 4 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net 1.I|4TB10 LO I/0 Hall TB redu MapReduce 4.5 A1 priori Zhao MapReduce Apriori Means speedup、 SIzeup、 scaleup3 Papadimitriou prioraL AprioriSome Dynamic Some &i Maple Generalized Sequential Pattern clustering GSP Secuen Distributed Co-clustering DisCo tial Pattern Discovery Using Equivalence Classes Hadoop SPADE S DisCo GB g Frequent Pattern-Projected Sequential Pattern Mapreduce Ⅵ lining FreeSpan50 KNN Prefix Projected Sequential Pattern Minir PrefixSpan. SPADF Ferreira Ma for Sequential Pattern Mining MEMISP dexing M Reduce LO 2 Sequential Pattern Mining with Reg Bow best of both Worlds ular expression Constraints SPIRIT 59 BoW BoW Havens C-mean C-mean 61 Sequential Pattern GSP GSP Mining Frequent Sequences MFS sP+ Mfs SPADE ncrementa quence Mining sm 21994-2018ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net ADABOOST PL LOGITBOOST PI ISM 63 Incremen Ⅵ maPreduce tal Frequent Sequences Mining IsE ISE Mapreduce mentally Updating Sequences IUS 65 IseI 66 Latent Tradeoff between performance and Difference Dirichlet Allocation LDa TPD Collapsed Gibbs Sampling CGs Collapsed variational Bayesian CVB CPU GPU 4.6 Graphic processing Unit GPU GPU luo SVM sM、 MapReduce Compute 60% Unified device architecture CuDa Mapreduce 2008 Shim mapReduce PDMiner parallel distributed miner apReduce MapReduce MapReduce 436076-8 g Generalized Linear Aggregates Distributed En Hefeeda gine GLADE. 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