Multi-Instance Learning with Distribution Change
Authors: Wei-Jia Zhang, Zhi-Hua Zhou
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that MICS is almost always significantly better than many state-of-the-art multi-instance learning algorithms when distribution change occurs; and even when there is no distribution change, their performances are still comparable. |
| Researcher Affiliation | Academia | Wei-Jia Zhang and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China {zhangwj,zhouzh}@lamda.nju.edu.cn |
| Pseudocode | No | The paper describes the approach mathematically and textually but does not include a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | First, we perform experiments on text data sets based on the 20 Newsgroups corpus popularly used in text categorization. |
| Dataset Splits | Yes | During the experiments, the parameters are selected via 5-folds cross validation on the training data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions) used for the implementation or experiments. |
| Experiment Setup | Yes | During the experiments, the parameters are selected via 5-folds cross validation on the training data. |