[1] |
KITAYAMA S , YAMAZAKI K . Simple estimate of the width in Gaussian kernel with adaptive scaling technique[J]. Applied Soft Computering, 2011,11(8):4726-4737.
|
[2] |
RODNER E , WACKER E S , KEMMLER M , et al . One-class classi-fication for anomaly detection in wire ropes with Gaussian processes in a few lines of code[A].Proceedings of the 12th IAPR Conference on Machine Vision Applications (MVA)[C]. Japan, 2010. 296-308.
|
[3] |
姚伏天 . 基于高斯过程的高光谱图像分类研究[D]. 杭州: 浙江大学, 2011.
|
[4] |
KAPOOR A , GRAUMAN K , URTASUN R , et al . Gaussian processes for object categorization[J].International Journal of Computer Vision, 2010,88(2):169-188.
|
[5] |
孙欣尧, 王雪, 王晟 . 无线传感网络协同概率多模识别方法[J]. 通信学报, 2011,32(6):141-147.
|
[6] |
熊志化 . 高斯过程模型及其在工业过程软测量中的应用研究[D]. 上海: 上海交通大学, 2006.
|
[7] |
VAN GOOL E , WINN W , ZISSERMAN A . The PASCAL visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010,88(2):303-338.
|
[8] |
陈凤 . 基于HRRP和JEM信号的雷达目标识别技术研究[D]. 西安: 西安电子科技大学, 2009.
|
[9] |
王磊, 邹北骥, 彭小宁 等 . 基于高斯过程的表情动作单元跟踪技术[J]. 电子学报, 2007,35(11):2087-2091.
|
[10] |
DEISENROTH M P , TURNER R D , HUBER M F , et al . Robust filtering and smoothing with Gaussian processes[J]. IEEE Transac-tions on Automatic Control, 2012,57(7):1865-1871.
|
[11] |
GASBARRA D , SOTTINEN T , ZANTEN H V . Conditional full support of Gaussian processes with stationary increments[J]. Journal of Applied Probability, 2011,48(2):561-568.
|
[12] |
RODNER E , DENZLER J . One-shot learning of object categories using dependent Gaussian processes[A]. Proceedings of the DAGM Conference on Pattern Recognition[C]. Springer,Heidelberg, 2010. 232-241.
|
[13] |
BOSCH A , ZISSERMAN A , MUNOZ X . Representing shape with a spatial pyramid kernel[A].ACM International Conference on Image and Video Retrieval (CIVR)[C]. Amsterdam,Netherlands, 2007. 401-408.
|
[14] |
CHUM O , ZISSERMAN A . An exemplar model for learning object classes[A]. ACM International Conference on Image and Video Re-trieval (CIVR)[C]. Amsterdam,Netherlands, 2007. 19-21.
|
[15] |
HAGERW W . Updating the inverse of a matrix[J]. Society for Indus-trial and Applied Mathematics (SIAM) Review, 1989,31(2):221-239.
|
[16] |
ADANKON M M , CHERIET M . Model selection for the LS-SVM application to handwriting recognition[J]. Pattern Recognition, 2009,42(12):3264-3270.
|
[17] |
CATANZARO B , SUNDARAM N , KEUTZER K . Fast support vector machine training and classification on graphics processors[A]. Pro-ceedings of the 25th International Conference on Machine Learn-ing(ICML)[C]. New York,NY,USA, 2008. 104-111.
|
[18] |
TOHME M , LENGELLE R . Maximum margin one class support vector machines for multiclass problems[J]. Pattern Recognition Let-ters, 2011,32(13):1652-1658.
|
[19] |
FENG W , XIE L , ZENG J , et al . Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models[J]. Journal of Visual Languages & Computing, 2009,20(3):188-195.
|
[20] |
RUIZ C , SPILIOPOULOU M , MENASALVAS E . Density-based semi-supervised clustering[J]. Data Mining and Knowledge Discovery, 2010,21(3):345-370.
|
[21] |
RASMUSSEN C E , WILLIAMS C K I . Gaussian Processes for Ma-chine Learning[M]. Cambridge: MIT Press, 2006.
|
[22] |
陈晓峰, 王士同, 曹苏群 . 监督多标记学习的基因功能分析[J]. 智能系统学报, 2008,3(1):83-90.
|
[23] |
KLAUS B , JOHANNS F , EYKE H . A unified model for multilabel classification and ranking[A].Proceedings of the 2006 Conference on ECAI 2006: 17th European Conference on Artificial Intelligence[C]. Riva del Garda,Italy, 2006. 489-493.
|