Towards Safe Semi-Supervised Learning for Multivariate Performance Measures
Authors: Yu-Feng Li, James Kwok, Zhi-Hua Zhou
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed method can effectively improve the safeness of SSL under multiple multivariate performance measures. |
| Researcher Affiliation | Academia | 1 National Key Laboratory for Novel Software Technology, Nanjing University 2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210023 3 Department of Computer Science & Engineering, Hong Kong University of Science and Technology, Hong Kong |
| Pseudocode | Yes | Algorithm 1 Cutting-plane algorithm for Eq.(7). |
| Open Source Code | No | The paper mentions a URL for S4VM (a baseline method) but does not provide a link or explicit statement about the open-source code for their proposed UMVP method. |
| Open Datasets | Yes | Downloaded from http://www.csie.ntu.edu.tw/~cjlin/ libsvmtools/datasets/, http://www.kyb.tuebingen.mpg.de/sslbook/, and from (Mallapragada et al. 2009) (for the ethn data set). For the MNIST data set, we focus on its four most difficult binary classification tasks (Zhang, Tsang, and Kwok 2007). |
| Dataset Splits | No | The paper specifies '1% of the samples are labeled and the rest are unlabeled' and that 'Each experiment is repeated 10 times, and the average performance on the unlabeled data is reported', but it does not define a distinct validation dataset split. |
| Hardware Specification | Yes | Experiments are run on a PC with a 3.2GHz Core2 Duo CPU and 4GB memory. |
| Software Dependencies | Yes | The experiments are used with MATLAB 8.0.1 and LIBLINEAR 1.91. |
| Experiment Setup | Yes | For all methods, the C parameter in SVM is set to 1 and the linear kernel is used. Parameters of S4VM are set as recommended in the package. ... ϵ in Algorithm 1 is set to 10 6. When Fβ is used as the performance measure, the c in Proposition 2 is set to the average number of positive samples for the multiple learners., i.e., 1 b b i=1(yi) 1. |