Online Kernel Selection via Incremental Sketched Kernel Alignment
Authors: Xiao Zhang, Shizhong Liao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies demonstrate that the proposed online kernel selection approach is computationally efficient while maintaining comparable accuracy for online kernel learning. |
| Researcher Affiliation | Academia | Xiao Zhang and Shizhong Liao School of Computer Science and Technology, Tianjin University, Tianjin 300350, China {xiaozhang, szliao}@tju.edu.cn |
| Pseudocode | Yes | Algorithm 1: OKS-ISKA Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We merged the training and testing data into a single dataset for each benchmark dataset4, and compared the proposed OKS-ISKA with the following state-of-the-art online kernel selection algorithms. 4http://www.csie.ntu.edu.cn/ cjlin/libsvmtools/datasets/ http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html |
| Dataset Splits | No | The paper mentions merging training and testing data and performing '20 different random permutations of the datasets' but does not provide specific percentages or counts for distinct training, validation, and test splits within these permutations for reproduction. |
| Hardware Specification | Yes | Algorithms were implemented in R 3.3.2 on a machine with 4-core Intel Core i7 3.60 GHz CPU and 16GB memory. |
| Software Dependencies | Yes | Algorithms were implemented in R 3.3.2 on a machine with 4-core Intel Core i7 3.60 GHz CPU and 16GB memory. |
| Experiment Setup | Yes | A set of Gaussian kernels with kernel widths σ {2 (i+1)/2, i = [ 12 : +2 : 12]} was adopted as the candidate kernel set and the kernel widths of OKL-GD were restricted to the same range. The initial parameter i of the kernel width was selected in { 12, 10, 8} uniformly... We tuned the stepsize of OGD in a range 10[ 5:+1:0] and the regularization parameter λ in a range 10[ 4:+1:1]. For our OKS-ISKA, we set q = 5 for the median estimate, µξ = 0.3, B = 150 for small datasets (T < 10, 000) and B = 200 for other datasets... Besides, we set ηt = 1/(tλ) as in Theorem 3. |