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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |