Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Transfer Feature Representation via Multiple Kernel Learning
Authors: Wei Wang, Hao Wang, Chen Zhang, Fanjiang Xu
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in two real-world applications verify the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1. Science and Technology on Integrated Information System Laboratory 2. State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences, Beijing 100190, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Transfer Feature Representation |
| Open Source Code | No | No mention of open-source code for the described methodology or a link to a repository was found. |
| Open Datasets | Yes | FERET (Phillips et al. 2000) and YALE (Belhumeur, Hespanha, and Kriegman 1997) are two public face data sets. |
| Dataset Splits | No | Specifically, we search σd based on the validation set in the range {0.1, 1, 10}, σ in the range {0.01, 0.1, 1, 10, 100} and λ in the range {0.1, 1, 10}. |
| Hardware Specification | No | No specific hardware details were found. |
| Software Dependencies | No | No specific software dependencies with version numbers were found. |
| Experiment Setup | Yes | TFR involves four parameters: σd, σ, λ and k. Specifically, we search σd based on the validation set in the range {0.1, 1, 10}, σ in the range {0.01, 0.1, 1, 10, 100} and λ in the range {0.1, 1, 10}. [...] The neighborhood size k for TFR is 3. Basis kernel functions are predetermined for TFR: linear kernel and Gaussian kernels with 10 different bandwidths, i.e., 0.5, 1, 2, 5, 7, 10, 12, 15, 17, 20. |