Hypothesis Sketching for Online Kernel Selection in Continuous Kernel Space
Authors: Xiao Zhang, Shizhong Liao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed hypothesis sketching approach significantly improves the efficiency of online kernel selection in continuous kernel space while retaining comparable predictive accuracies. |
| Researcher Affiliation | Academia | Xiao Zhang and Shizhong Liao College of Intelligence and Computing, Tianjin University, Tianjin 300350, China szliao@tju.edu.cn |
| Pseudocode | Yes | Algorithm 1 OKS-SIL Algorithm |
| Open Source Code | No | The paper does not contain any statement about releasing their source code or a link to a repository for their method. |
| Open Datasets | Yes | For each benchmark dataset4 we merged the training and testing data into a single dataset. ... 4http://www.csie.ntu.edu.cn/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | But in online learning settings, there is no such delineation among training, validation and testing [Diethe and Girolami, 2013; Zhang et al., 2019]. |
| Hardware Specification | Yes | all the algorithms were implemented in R 3.3.2 on a PC with 3.60 GHz Intel Core i7 CPU and 16GB memory. |
| Software Dependencies | Yes | all the algorithms were implemented in R 3.3.2 on a PC with 3.60 GHz Intel Core i7 CPU and 16GB memory. |
| Experiment Setup | Yes | For our OKS-SIL, we set B = 150 for the small datasets (T < 104), B = 200 for the other datasets (T ≥ 104), and ν = 0.9, s = 3 according to the empirical analysis in the subsection Parameter Influence . We used a time-varying stepsize ρt = 1/t at round t for optimal kernel updating, and tuned the stepsize of KOGD in a range 10[−5:+1:0] for all the algorithms. |