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[...] avoiding performance deterioration using unlabeled data, or safe semi-supervised regression algorithm [1]. You will find an example of using this code in the 'example.m' function. The example data is housing data. In particular, 10 examples are labeled and the rest are unlabeled. In our AAAI'17 experiment, all the features and labels are normalized to [0,1] in advanced. Reference :
[1] Yu-Feng Li, Han-Wen Zha and Zhi-Hua Zhou. Learning [...]
[...] and effectively. A Readme file and some data files are included in the package. References : 1. X.-S. Wei, J. Wu and Z.-H. Zhou. Scalable Algorithms for Multi-Instance Learning. IEEE Transactions on Neural Networks and Learning Systems, in press. 2. X.-S. Wei, J. Wu and Z.-H. Zhou. Scalable Multi-Instance Learning. In: Proceedings of the 14th International Conference on Data Mining (ICDM’14), Shenzhen, China, 2014, pp.1037-1042. ATTN : This packages are free for academic usage. You can run them at your own risk. For other purposes, please contact Prof. Jianxin Wu (wujx2001@gmail.com). [...]
[...] a real-world object is associated with multiple instances as well as multiple labels simultaneously.
MIMLfast This package includes the MATLAB codes of MIMLfast, which is [...]
[...] learning.
MIMLWEL This package includes the MATLAB code of the multi-instance multi-label learning algorithm MIMLWEL.
MissSVM MissSVM is a package for solving multi-instance [...]
[...] Description :
This package includes the MATLAB code of One-Pass AUC Optimization (OPAUC).
Reference : [1] Wei Gao, Rong Jin, Shenghou Zhu and Zhi-Hua Zhou. One-Pass AUC Optimzation. In: Proceedings [...]
[...] . For any problem concerning the code, please feel free to contact Mr. Wang. Download : [ code ](1.97M)
[...] fine-grained image retrieval. A Readme file and some data files are included in the package. Reference :
[1] X.-S. Wei, J.-H. Luo, J. Wu and Z.-H. Zhou. Selective Convolutional Descriptor Aggregation [...]
[...] sophisticated optimization problems, while are hard to scale to high dimensionality (e.g., larger than 1,000). Previously, the random embedding technique has been shown successful for solving [...]
[...] Photos 江阴 2008 5 photos 国防园 2008 5 photos 植物园 2007 5 photos 上庄园 2007 4 photos 栖霞山 2006 1 photo 情侣园 2006 3 photos 连云港 2005 3 photos [...]
[...] codes. For any problem concerning the codes, please feel free to contact Ms. Xu. Download : [ code ] (1.96MB)
[...] patch sizes and 43 data sets) configurations of experiments we make two significant new observations: (1) Bag generators with a dense sampling strategy perform better than those with other strategies; [...]
[...] paid to streaming features. The critical challenges for online streaming feature selection include (1) the continuous growth of feature volumes over time, (2) a large feature space, possibly [...]