A General Formulation for Safely Exploiting Weakly Supervised Data
Authors: Lan-Zhe Guo, Yu-Feng Li
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple weakly supervised learning tasks such as label noise learning, domain adaptation and semi-supervised learning validate the effectiveness of our proposed algorithms. |
| Researcher Affiliation | Academia | Lan-Zhe Guo, Yu-Feng Li National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {guolz, liyf}@lamda.nju.edu.cn |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | We call our proposal as SAFEW (SAFE Weakly supervised learning)1. 1http://lamda.nju.edu.cn/code/SAFEW.ashx |
| Open Datasets | Yes | We conduct experimental comparison for label noise learning tasks on a number of frequently-used classification datasets2, i.e., Australian, Breast-Cancer, Diabetes, Digit1, Heart, Ionosphere, USPS and Splice. 2https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | Yes | For each data set, 80% of instances are used for training and the rest ones are used for testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'glmfit function in Matlab' and 'Libsvm package (Chang and Lin 2011)' but does not provide specific version numbers for these software components or any other dependencies. |
| Experiment Setup | Yes | For k-NN method, k is set to 3. |