Active Learning by Learning
Authors: Wei-Ning Hsu, Hsuan-Tien Lin
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical studies of the resulting ALBL algorithm confirm that it performs better than state-of-the-art strategies and a leading blending algorithm for active learning, all of which are based on human-designed philosophy. |
| Researcher Affiliation | Academia | Wei-Ning Hsu Department of Electrical Engineering, National Taiwan University mhng1580@gmail.com Hsuan-Tien Lin Department of Computer Science and Information Engineering, National Taiwan University htlin@csie.ntu.edu.tw |
| Pseudocode | Yes | Algorithm 1 ACTIVE LEARNING BY LEARNING |
| Open Source Code | No | The paper does not provide any explicit statement or link for the open-source code of the ALBL methodology described. |
| Open Datasets | Yes | We take six real-world data sets, liver, sonar, vehicle, breast, diabetes, heart) from the UCI Repository (Bache and Lichman 2013). |
| Dataset Splits | No | The paper states 'we reserve 80% of the instances as the training set, and retain the other 20% as the test set,' but does not specify a separate validation dataset split. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Lib SVM (Chang and Lin 2011)' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | We take SVM (Vapnik 1998) as the underlying classifier and use Lib SVM (Chang and Lin 2011) with all the default parameters to train the classifier. |