Optimal Margin Distribution Machine for Multi-Instance Learning
Authors: Teng Zhang, Hai Jin
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the superiority of the proposed method. and We empirically study our method on CBIR image data sets in Sec. 5.1 and benchmark data sets in Sec. 5.2, respectively. |
| Researcher Affiliation | Academia | Teng Zhang and Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab, Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, China {tengzhang, hjin}@hust.edu.cn |
| Pseudocode | Yes | Algorithm 1 MI-ODM |
| Open Source Code | No | The paper does not provide any specific links to open-source code or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | We empirically study our method on CBIR image data sets in Sec. 5.1 and benchmark data sets in Sec. 5.2, respectively. and We have also evaluated our method on five benchmark data sets commonly used in the literature of MIL, i.e., Musk1, Musk2, Elephant, Fox and Tiger. |
| Dataset Splits | Yes | The training/test split are repeated for 10 times. and All the parameters are selected by 5-fold cross validation. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications, or detailed computer specs) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) were provided in the paper. |
| Experiment Setup | Yes | The parameters C1, C2, λ1, λ2 are selected from {1, 10, 100, 1000}, and ν, θ are selected from {0.2, 0.4, 0.6, 0.8}. The RBF kernel is applied for all the methods and the width is selected from the set of {2 4δ, 2 2δ, 20δ, 22δ, 24δ}, where δ is the reciprocal of dimension. |