Theoretical Analysis of Label Distribution Learning
Authors: Jing Wang, Xin Geng5256-5263
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we rethink LDL from theoretical aspects, towards analyzing learnability of LDL. Firstly, risk bounds for three representative LDL algorithms (AA-k NN, AA-BP and SA-ME) are provided. As far as we know, this is perhaps the first research on theory of LDL. |
| Researcher Affiliation | Academia | Jing Wang, Xin Geng MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering, Southeast University, Nanjing 210096, China {wangjing91, xgeng}@seu.edu.cn |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured steps presented in a code-like format. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or include any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving specific datasets. It mentions a 'training set S' as part of its theoretical notation for analyzing LDL algorithms, but does not provide access information for any public dataset. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not describe empirical experiments, therefore it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe the execution of experiments, therefore it does not provide any hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not describe the setup of any empirical experiments, thus no hyperparameters or system-level training settings are provided. |