Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Kernel Selection via Spectral Analysis
Authors: Jian Li, Yong Liu, Hailun Lin, Yinliang Yue, Weiping Wang
IJCAI 2017 | Venue PDF | 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. |