Computer Science ›› 2019, Vol. 46 ›› Issue (5): 157-162.doi: 10.11896/j.issn.1002-137X.2019.05.024
Previous Articles Next Articles
LI Wen-hai1,2, CHENG Jia-yu2, XIE Chen-yang2
CLC Number:
[1]GHAREHBAGHI A,ASK P,BABIC A.A pattern recognition framework for detecting dynamic changes on cyclic time series [J].Pattern Recognition,2015,48(3):696-708. [2]CHEN T T,LEE S J.A weighted ls-svm based learning system for time series forecasting [J].Information Sciences,2015,299(1):99-116. [3]KANG P,CHO S.Locally linear reconstruction for instance-based learning [J].Pattern Recognition,2008,41(1):3507-3518. [4]BOUSQUET O,ELOSSEEFF A.Stability and generalization[J].Journal of Machine Learning Research,2001,3(2):499-526. [5]PETITJEAN F,KETTERLIN A,GANCARSKI P.A global ave-raging method for dynamic time warping,with applications toclustering [J].Pattern Recognition,2011,44(1):678-693. [6]VIKJORD V V,JESSEN R.Information theoretic clusteringusing a k-nearest neighbors approach [J].Pattern Recognition,2014,47(9):3070-3081. [7]ROJAS I,VALENZUELA O,ROJAS F,et al.Soft-computingtechniques and arma model for time series prediction [J].Neurocomputing,2008,71(4):519-537. [8]SUYKENS J A K,BRABANTER J D,LUKAS L,et al.Weighted least squares support vector machines:robustness andsparse approximation [J].Neurocomputing,2002,48(1-4):85-105. [9]XU H.Robustness and regularization of support vector ma-chines [J].Journal of Machine Learning Research,2009,10(3):1485-1510. [10]SANTOS J D A,BARRETO G A.A regularized estimationframework for online sparse lssvr models [J].Neurocomputing,2017,238(1):1-12. [11]CLÁUDIO R D S,SOARES C,KNOBBE A.Entropy-based discretization methods for ranking data [J].Information Sciences,2016,329(1):921-936. [12]PARK H J,PARK W J,JUNG T,et al.On general purpose time series similarity measures andtheir use as kernel functions in support vector machines [J].Information Sciences,2014,281(4):478-495. [13]WU H H,LIU G H,WANG W.The problem of similaritymatching for uncertain time series[J].Computer Research and Development,2014,51(8):1802-1810.(in Chinese)吴红花,刘国华,王伟.不确定时间序列的相似性匹配问题[J].计算机研究与发展,2014,51(8):1802-1810. [14]HAN Z M.Research on Effective Clustering Algorithm for Hot Topic Time Series[J].Journal of Computer,2012,35(11):2337-2347.(in Chinese)韩忠明.面向热点话题时间序列的有效聚类算法研究[J].计算机学报,2012,35(11):2337-2347. [15]YUAN J D,WNAG Z H.Time Series Representation in Classification Algorithms[J].Computer Science,2015,42(3):1-7.(in Chinese)原继东,王志海.时间序列的表示于分类算法综述[J].计算机科学,2015,42(3):1-7. [16]HAN M,XU M L,MU D Y.Application of Non-nuclear Correlation Vector Machine in Time Series Prediction[J].Journal of Computer,2014,37(12):2427-2432.(in Chinese)韩敏,徐美玲,穆大芸.无核相关向量机在时间序列预测中的应用[J].计算机学报,2014,37(12):2427-2432. [17]CHEN Y,SHI Z H.Mixed Financial Time Series ForecastingModel Based on Adaboost and Regularized ELM and Its Application[J].Mathematical Statistics and Management,2017,36(1):112-124.(in Chinese)陈艳,石智慧.基于Adaboost和正则化ELM的混合金融时间序列预测模型及其应用[J].数理统计与管理,2017,36(1):112-124. [18]HAO Y H,ZHANG H F.Incremental Learning Method Based on Double Support Vector Regression Machine[J].Computer Science,2016,43(2):230-235.(in Chinese)郝运河,张浩峰.基于双支持向量回归机的增量学习方法[J].计算机科学,2016,43(2):230-235. |
[1] | SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69. |
[2] | CHAI Hui-min, ZHANG Yong, FANG Min. Aerial Target Grouping Method Based on Feature Similarity Clustering [J]. Computer Science, 2022, 49(9): 70-75. |
[3] | HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82. |
[4] | ZHENG Wen-ping, LIU Mei-lin, YANG Gui. Community Detection Algorithm Based on Node Stability and Neighbor Similarity [J]. Computer Science, 2022, 49(9): 83-91. |
[5] | WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220. |
[6] | WANG Run-an, ZOU Zhao-nian. Query Performance Prediction Based on Physical Operation-level Models [J]. Computer Science, 2022, 49(8): 49-55. |
[7] | LI Bin, WAN Yuan. Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment [J]. Computer Science, 2022, 49(8): 86-96. |
[8] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
[9] | LI Rong-fan, ZHONG Ting, WU Jin, ZHOU Fan, KUANG Ping. Spatio-Temporal Attention-based Kriging for Land Deformation Data Interpolation [J]. Computer Science, 2022, 49(8): 33-39. |
[10] | WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48. |
[11] | ZHAO Dong-mei, WU Ya-xing, ZHANG Hong-bin. Network Security Situation Prediction Based on IPSO-BiLSTM [J]. Computer Science, 2022, 49(7): 357-362. |
[12] | SHUAI Jian-bo, WANG Jin-ce, HUANG Fei-hu, PENG Jian. Click-Through Rate Prediction Model Based on Neural Architecture Search [J]. Computer Science, 2022, 49(7): 10-17. |
[13] | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang. Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [J]. Computer Science, 2022, 49(7): 40-49. |
[14] | ZHANG Hong-bo, DONG Li-jia, PAN Yu-biao, HSIAO Tsung-chih, ZHANG Hui-zhen, DU Ji-xiang. Survey on Action Quality Assessment Methods in Video Understanding [J]. Computer Science, 2022, 49(7): 79-88. |
[15] | ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112. |
|