Training-Efficient Feature Map for Shift-Invariant Kernels
Authors: Xixian Chen, Haiqin Yang, Irwin King, Michael R. Lyu
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we provide empirical results. Finally, in Section 5, we conclude the whole work. and 4 Empirical Evaluation In this section, we empirically demonstrate the superiority of the proposed methods on the Gaussian kernel matrix approximation and related regression task. |
| Researcher Affiliation | Academia | Xixian Chen1,2, Haiqin Yang1,2, Irwin King1,2, Michael R. Lyu1,2 1Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China 2Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong {xxchen, hqyang, king, lyu}@cse.cuhk.edu.hk |
| Pseudocode | Yes | Algorithm 1: TEFM-G and Algorithm 2: TEFM-S |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We use six publicly available real-world datasets listed in Table 2, and they can be downloaded from LIBSVM website [Chang and Lin, 2011] or UCI machine learning repository. |
| Dataset Splits | No | The paper mentions 'The parameters are chosen by cross-validation' but does not provide specific details regarding dataset splits for training, validation, or testing (e.g., percentages, absolute counts, or number of folds for cross-validation). |
| Hardware Specification | No | The paper states 'All our experiments are run on Matlab with single thread mode' but does not specify any hardware details like CPU, GPU, or memory used for the experiments. |
| Software Dependencies | No | The paper states 'All our experiments are run on Matlab' but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | No | The paper mentions 'The parameters are chosen by cross-validation' but does not explicitly provide specific hyperparameter values (e.g., learning rate, batch size, epochs) or detailed system-level training settings. |