Eigenvalues Ratio for Kernel Selection of Kernel Methods
Authors: Yong Liu, Shizhong Liao
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 {yongliu,szliao}@tju.edu.cn |
| 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... |