Approximate Vanishing Ideal via Data Knotting
Authors: Hiroshi Kera, Yoshihiko Hasegawa
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experimental classification tests, our method discovered much fewer and lower-degree polynomials than an existing state-of-the-art method. Consequently, our method accelerated the runtime of the classification tasks without degrading the classification accuracy. |
| Researcher Affiliation | Academia | Hiroshi Kera, Yoshihiko Hasegawa Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan |
| Pseudocode | Yes | The paper includes 'Algorithm 1 Find Basis', 'Algorithm 2 Exact Vanish Pursuit', and 'Algorithm 3 Main'. |
| Open Source Code | No | The paper mentions 'Python implementation' but does not provide any explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The datasets were downloaded from UCI machine learning repository (Lichman 2013). |
| Dataset Splits | Yes | The hyperparameters were determined by 3-fold cross validation and the results were averaged over ten independent runs. In each run, the datasets were randomly split into training (60%) and test (40%) datasets. |
| Hardware Specification | No | The paper states 'Bothe methods were tested by Python implementation on a workstation with four processors and 8GB memory.' This provides some general details, but lacks specific model numbers for the processors or any GPU information, which are required for full reproducibility. |
| Software Dependencies | No | The paper mentions 'Python implementation' but does not specify version numbers for Python or any associated libraries/dependencies used in the experiments. |
| Experiment Setup | No | The paper states that 'The hyperparameters were determined by 3-fold cross validation', but it does not explicitly provide the specific values of these hyperparameters (e.g., learning rate, batch size, epochs, optimizer settings) or other detailed experimental configuration parameters. |