Multi-Instance Learning with Key Instance Shift
Authors: Ya-Lin Zhang, Zhi-Hua Zhou
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments are performed on both synthetic data and real world datasets. We compare the proposed method with many state-of-the-art algorithms... |
| Researcher Affiliation | Academia | Ya-Lin Zhang and Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023, China {zhangyl, zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Instance Prototype Learning for MIKI |
| Open Source Code | No | The paper does not provide a direct statement or link for the open-source code of the proposed method. |
| Open Datasets | No | The paper mentions using '20 Newsgroups corpora' and 'benchmark datasets' (Musk1, Musk2, Elephant, Fox, Tiger) but does not provide specific links, DOIs, or formal citations for public access to the exact datasets or splits used. |
| Dataset Splits | Yes | We follow the typically deliberately biased sampling procedure [Zadrozny, 2004] to separate the bags into disjoint training and test sets. Specifically, we define a random variable si for each bag... Pr(si = 1|xi SC1) = a , Pr(si = 1|xi SC2) = b . Here, SC1 and SC2 denote different sub-concepts (i.e.,P1 and P2) and a = 0.8, b = 0.2. Furthermore, we conduct ten times 10-fold cross validations as previous studies done. |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'RBF kernel is used for all SVM-based methods' and 'multi-class SVM model' but does not specify any software names with version numbers (e.g., Python, scikit-learn, PyTorch versions) used for implementation. |
| Experiment Setup | Yes | For MIKI, we simply set K to 5 for synthetic dataset and 10 for the other datasets without any tuning, and set max iteration to 5 to accelerate the method. Other parameters are selected via 5-fold cross validation. |