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..
Improving Semi-Supervised Target Alignment via Label-Aware Base Kernels
Authors: Qiaojun Wang, Kai Zhang, Guofei Jiang, Ivan Maric
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section compares our method with a number of state-of-the-art algorithms for semi-supervised kernel design, for both classification and regression. ... Results are reported in Table 1. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854 USA EMAIL, EMAIL 2NEC Laboratories America, Inc. 4 Independence Way, Princeton, NJ 08540 USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Input: labeled samples Xl = {xi}l i=1, unlabeled sample set Xu = {xi}n i=l+1; Gaussian Kernel k( , ), label Y = [y1, y2, ..., yc] Rl c. |
| Open Source Code | No | No statement explicitly providing open-source code for the methodology, or a link to a code repository, was found. |
| Open Datasets | Yes | This section compares our method with a number of state-of-the-art algorithms for semi-supervised kernel design, for both classification and regression. ... Digit1, USPS, COIL2, BSI, COIL, g241n, Text, usps38, usps49, usps56, usps27, odd/even ... The task is indoor location estimation using received signal strength(RSS) that a client device received from Wi-Fi access points (Yang, Pan, and Zheng 2000). |
| Dataset Splits | No | No specific training/validation/test dataset splits with exact percentages, sample counts, or citations to predefined validation splits were found. The paper mentions '50 labeled samples randomly chosen for each class' and training/test set usage, but not a distinct validation split. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments were provided. |
| Software Dependencies | No | The paper mentions 'libsvm package' but does not provide a specific version number. No other software dependencies with version numbers are listed. |
| Experiment Setup | Yes | For the kernel width, we first compute b0 as the inverse of the average squared pairwise distances, and then choose b among b0 { 1/5, 1, 5, 10} that gives the best performance. The parameter δ and ϵ are chosen from {10^-5, 10^-3, 10^-1, 1}. ... The regularization parameter C is chosen as{0.1, 1, 10, 100, 1000, 10000}. |