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基于贝叶斯网络的半监督聚类集成模型
- 已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca
ObjectLocalization_Code
- 一个基于Felzenszwalb的latent svm的目标检测框架-This is an implementation of our object localization system as described in [1]. This system is an adaption of the object detection framework of Felzenszwalb et al. [2][3](http://people.cs.uchicago.edu/~pff/latent-r
voc-release5
- Latent SVM code voc-release 5
algorithm
- Latent SVM算法实现行人检测；需要opencv库-Latent SVM algorithm for pedestrian detection needing opencv library
Action-Detection
- This paper advances prior work by proposing a joint learning framework to simultaneously identify the spatial and temporal extents of the action of interest in training videos. To get pixel-level localization results, our method uses dense traj