A Bayesian Framework for Online Classifier Ensemble
Authors: Qinxun Bai, Henry Lam, Stan Sclaroff
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments with real-world datasets, our formulation often performs better than online boosting algorithms. (Abstract) and Section 5: Experiments |
| Researcher Affiliation | Academia | Qinxun Bai QINXUN@CS.BU.EDU Department of Computer Science, Boston University, Boston, MA 02215 USA Henry Lam KHLAM@BU.EDU Department of Mathematics and Statistics, Boston University, Boston, MA 02215 USA Stan Sclaroff SCLAROFF@CS.BU.EDU Department of Computer Science, Boston University, Boston, MA 02215 USA |
| Pseudocode | Yes | Algorithm 1 Bayesian Ensemble and Algorithm 2 Closed-form Bayesian Ensemble |
| Open Source Code | No | No explicit statement or link providing access to the paper's own source code is found. |
| Open Datasets | Yes | We report two sets of experiments on binary classification benchmark datasets1. ... 1http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | Yes | Each data set is split into training and testing sets for each random trial, where a training set contains no more than 10% of the total amount of data. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory, or processing units) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) are mentioned. |
| Experiment Setup | Yes | In all experiments, we have set the hyperparameters of our method α = β = 1 and θ = 0.1. |