R-SVM+: Robust Learning with Privileged Information
Authors: Xue Li, Bo Du, Chang Xu, Yipeng Zhang, Lefei Zhang, Dacheng Tao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on realworld datasets demonstrate the necessity of studying robust SVM+ and the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Academia | Xue Li1,2, Bo Du 1, Chang Xu3, Yipeng Zhang1, Lefei Zhang1, Dacheng Tao3 1 School of Computer Science, Wuhan University, China 2 LIESMARS, Wuhan University, China 3 UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney, Australia |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The MNIST+ dataset [Vapnik and Vashist, 2009], the RGB-D Face dataset [Hg et al., 2012], and the Human Activity Recognition dataset [Anguita et al., 2013]. |
| Dataset Splits | Yes | For the MNIST+ dataset, it is randomly split into a training set of 100 images, a test set of 1866 images, and a validation set of 4002 [Vapnik and Vashist, 2009]. For the RGB-D Face dataset, ... We randomly split 40% color and corresponding depth image pairs per class for training, 30% image pairs per class for testing, and the rest 30% for validation for 10 times. ... The remaining examples from the desired class and the same number of examples from the rest of classes are used as the validation examples. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper mentions that the quadratic programming problem can be 'efficiently optimized using off-the-shelf solvers' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific solver libraries with their versions). |
| Experiment Setup | Yes | For all the methods, the regularization parameter C are selected from 10{ 2,1,0,1,2} and the Gaussian kernel is used. For SVM+, L2-SVM+ and the proposed R-SVM+, we set the parameter of Gaussian kernel γ = 1 D where D is the mean of distances among examples in the training set according to [Li et al., 2016]. While for SVM and RSVM-RHHQ, γ is selected from 10{ 3, 2, 1,0,1,2,3}. For RSVM-RHHQ, the scaling constant η is varied in range of {0.01, 0.1, 0.5, 1, 2, 3, 10, 100}. For SVM+-based methods, the trade-off parameter ρ is selected from 10{ 2, 1,0,1,2}. For the proposed R-SVM+, we also vary the parameter σ in range of {5, 10, 50, 100} and λ in range of 10{ 5, 4,...,0,1}. The best parameters for all methods are determined with a joint cross validation model selection strategy on the validation set. |