Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
Authors: Yun Liao, Junfan Li, Shizhong Liao, Qinghua Hu, Jianwu Dang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Ahpatron outperforms the state-of-the-art algorithms on the same or a smaller budget. Table 2: Comparison with the state-of-the-art algorithms |
| Researcher Affiliation | Academia | Yun Liao, Junfan Li, Shizhong Liao , Qinghua Hu, Jianwu Dang College of Intelligence and Computing, Tianjin University, Tianjin 300350, China {yliao,junfli,szliao,huqinghua}@tju.edu.cn, jdang@jaist.ac.jp |
| Pseudocode | Yes | Algorithm 1: AVP, Algorithm 2: Ahpatron |
| Open Source Code | Yes | Codes and datasets: https://github.com/alg4ml/Ahpatron.git |
| Open Datasets | Yes | We download six binary classification datasets from UCI machine learning repository 4 and LIBSVM website 5, as shown in Table 1. 4http://archive.ics.uci.edu/ml/datasets.php 5https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ binary.html |
| Dataset Splits | No | The paper does not specify exact train/validation/test splits or percentages. It describes online learning where examples are processed sequentially, which typically does not involve fixed validation splits in the same manner as batch learning. |
| Hardware Specification | Yes | All algorithms are implemented in R on a Windows machine with 2.8 GHz Core(TM) i7-1165G7 CPU 6. |
| Software Dependencies | No | The paper states "All algorithms are implemented in R" but does not provide a specific version number for R or any other software dependencies with their versions. |
| Experiment Setup | Yes | For BOGD++, NOGD, and FOGD, we choose the stepsize of gradient descent from n 10[ 3:1:3] o . The other parameters of BOGD++ and NOGD follow the original paper. All parameters of POMDR also follow the original paper. For Projectron and Projectron++, there is a parameter 0 < η < 1 balancing the memory costs and prediction performance. We choose η {0.1, 0.9}. For Ahpatron, we set the parameters following Theorem 6, that is, η = 0.0005, λ = U B 2 . We choose the best ε {0.5, 0.6, 0.7, 0.8, 0.9} in hindsight, and set σ = 1 for all datasets. If the per-round running time of Projectron++ is larger than 1 hour, then we set σ = 2. |