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..
Eigenvalues Ratio for Kernel Selection of Kernel Methods
Authors: Yong Liu, Shizhong Liao
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis and experimental results demonstrate that our kernel selection criterion is a good choice for kernel selection.In this section, we will empirically analyze the performance of our proposed eigenvalues ratio criterion (ER). |
| Researcher Affiliation | Academia | Yong Liu and Shizhong Liao School of Computer Science and Technology, Tianjin University, Tianjin 300072, P. R. China EMAIL |
| Pseudocode | No | The paper describes methods and theoretical analysis but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The evaluation is made on 8 available public data sets from LIBSVM data seen in Table 1. |
| Dataset Splits | Yes | For each data set, we run all methods 30 times with randomly selected 70% of all data for training and the other 30% for testing.The optimal values for the parameters t {1, 4, 16} and η {0.2, 0.6, 1} of ER, and the parameter δ {2i, i = 0, 5, 10, 15, 20} of EP ... are determined by 3-fold cross-validation on the training set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or specific machine configurations) used to run the experiments. |
| Software Dependencies | No | The paper mentions using LSSVM but does not provide specific software names with version numbers for its implementation or any other software dependencies. |
| Experiment Setup | Yes | We use the popular Gaussian kernels K(x, x ) = exp[-τ||x - x'||^2] as our candidate kernels, τ {2^i, i = -15, -14, . . . , 15}. The learning machine we used is LSSVM. For each kernel selection criterion and each training set, we chose the optimal kernel parameter τ for each fixed regularized parameter λ {0.01, 0.1, 1, 10}. The optimal values for the parameters t {1, 4, 16} and η {0.2, 0.6, 1} of ER, and the parameter δ {2i, i = 0, 5, 10, 15, 20} of EP... |