The Ostomachion Process
Authors: Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on relational modeling and decision tree classification have validated the merit of the OP. |
| Researcher Affiliation | Academia | Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen Machine Learning Research Group, National ICT Australia, Eveleigh, NSW 2015, Australia {xuhui.fan, bin.li, yi.wang, yang.wang, fang.chen}@nicta.com.au |
| Pseudocode | Yes | Algorithm 1 RJMCMC for the Ostomachion process |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We test our ORM on seven benchmark data sets: Foodweb, Dolphin, Lazega, Polbooks, Train, Reality, Wikitalk. ... Lichman, M. 2013. UCI machine learning repository. |
| Dataset Splits | Yes | The performance of the two applications is evaluated by averaging the prediction on 10 randomly selected (in a ratio of 1/10) hold-out test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper refers to algorithms and models (e.g., 'reversible-jump MCMC algorithm', 'decision tree CART') and their implementations ('RJMCMC-2', 'RJMCMC-3'), but does not specify any software libraries or dependencies with version numbers. |
| Experiment Setup | Yes | In our experiments, we set the time limit τ = 5 (meaning that the expected number of cuts is 5) and the concentration parameter α = 10. We set T = 200 (200 iterations) for both RJMCMC-2 and RJMCMC-3. |