Efficient Kernel Selection via Spectral Analysis
Authors: Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on lots of data sets show that our proposed criterion can not only give the comparable results as the state-of-the-art criterion, but also significantly improve the efficiency. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences 3Tianjin University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the described methodology. |
| Open Datasets | Yes | The evaluation is made on 25 publicly available data sets from UCI, Stat Lib and Weka Collections seen in Table 2. |
| Dataset Splits | Yes | For each data set, we run all methods 50 times with randomly selected 70% of all data for training and the other 30% for testing. |
| Hardware Specification | Yes | Experiments are conducted on a Dell PC with 3.1-GHz 4-core CPU and 4-GB memory. |
| Software Dependencies | No | The paper mentions using 'LSSVM' and 'Gaussian kernels' but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We use the popular Gaussian kernels K(x, x ) = exp x x 2 2 2τ as our candidate kernels, τ {2i, i = 15, 14, . . . , 15}. ... we set the regularization parameter λ = 1 for all methods. ... In this experiment, we set r = 3. |