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..
AccGenSVM: Selectively Transferring from Previous Hypotheses
Authors: Diana Benavides-Prado, Yun Sing Koh, Patricia Riddle
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we present experiments and discuss results for binary classification using Acc Gen SVM and other publicly available HTL methods for homogeneous transfer. |
| Researcher Affiliation | Academia | Diana Benavides-Prado Dept. of Computer Science The University of Auckland EMAIL; Yun Sing Koh Dept. of Computer Science The University of Auckland EMAIL; Patricia Riddle Dept. of Computer Science The University of Auckland EMAIL |
| Pseudocode | Yes | Algorithm 1: Pseudo-code for transfer with Acc Gen SVM |
| Open Source Code | Yes | Acc Gen SVM1 is built on top of Lib SVM [Chang and Lin, 2011], using available KL divergence [Hausser and Strimmer, 2014] and FNN implementations [Beygelzimer et al., 2013]. 1Software available at: https://github.com/nanarosebp/ Ph DProject/tree/master/Acc Gen SVM |
| Open Datasets | Yes | Caltech256 is a benchmark dataset on image recognition... Image Net is a large benchmark dataset... Office is a small dataset... Caltech-256 object category dataset. [Griffin et al., 2007] |
| Dataset Splits | No | The paper mentions training and test splits, but no explicit validation set split is described. 'Transfer level: we extract random samples of sizes 10%, 20% and 30% for training. ... We also select independent random binary samples of size 20%, 30%, and 50% as test sets.' |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models or memory specifications) used for running experiments are provided. |
| Software Dependencies | Yes | Acc Gen SVM1 is built on top of Lib SVM [Chang and Lin, 2011], using available KL divergence [Hausser and Strimmer, 2014] and FNN implementations [Beygelzimer et al., 2013]. For FNN, 'R package version 1.1, 2013.' is cited. For KL divergence, 'R package v.1.2.1.' is cited. |
| Experiment Setup | Yes | Based on a sensitivity analysis of the regularization parameter C and the γ parameter for the RBF kernel, we set C = 1 and γ = 1/f, with f number of features. We use a KL divergence threshold of 0.3 for all datasets. For FNN, we work with 3 nearest neighbours. |