Efficient Online Model Adaptation by Incremental Simplex Tableau
Authors: Zhixian Lei, Xuehan Ye, Yongcai Wang, Deying Li, Jia Xu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Performance evaluations of FAST-IST were conducted by both simulations and actual experimental data. Efficiency and accuracy are evaluated as the key performance for online adaptation. Evaluation by Activity Recognition Dataset Experiment Settings Human Activity Recognition Using Smartphones Data Set from open data set UCI (Lichman 2013) is used to evaluate Fast-IST in actual experiment. |
| Researcher Affiliation | Academia | Zhixian Lei,1 Xuehan Ye,2 Yongcai Wang,2 Deying Li,2 Jia Xu3 1 Department of Computer Sciences, Harward University, USA; 2 Department of Computer Sciences, Renmin University of China, Beijing; 3 Department of Computer Sciences, City University of New York, USA |
| Pseudocode | No | The paper describes algorithms in text but does not provide structured pseudocode blocks or formally labeled algorithm sections. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for its methodology. |
| Open Datasets | Yes | Human Activity Recognition Using Smartphones Data Set from open data set UCI (Lichman 2013) is used to evaluate Fast-IST in actual experiment. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages, sample counts, or methodology for training, validation, and test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | In simulations the batch size is set to 10. and fixed-iteration IST is used in model update, in which the number of iterations in Simplex is set to a constant C. The correct probability of m basic classifiers are set uniformly distributed from 55% to 80% to simulate BCs of different accuracy. |