Learning Transformations for Classification Forests
Authors: Qiang Qiu; Guillermo Sapiro
ICLR 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical and experimental results support the proposed framework. This section presents experimental evaluations using public datasets: the MNIST handwritten digit dataset, the Extended Yale B face dataset, and the 15-Scenes natural scene dataset. |
| Researcher Affiliation | Academia | Qiang Qiu QIANG.QIU@DUKE.EDU Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA Guillermo Sapiro GUILLERMO.SAPIRO@DUKE.EDU Department of Electrical and Computer Engineering, Department of Computer Science, Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA |
| Pseudocode | No | The paper describes algorithms and processes (e.g., gradient descent, K-SVD) but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | This section presents experimental evaluations using public datasets: the MNIST handwritten digit dataset, the Extended Yale B face dataset, and the 15-Scenes natural scene dataset. |
| Dataset Splits | No | The paper mentions a 'validation process' to choose tree depth but does not provide specific details on a validation dataset split (e.g., percentages or counts) separate from training and testing splits. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper mentions methods like 'K-SVD' and 'C4.5' but does not specify any software names with version numbers or other dependencies needed for replication. |
| Experiment Setup | Yes | We train 20 classification trees with a depth of 9, each using only 10% randomly selected training samples. We train 20 classification trees with a depth of 5, each using all training samples. We train 30 classification trees with a depth of 9, each using 5% randomly selected training samples. |