Learning Instance Concepts from Multiple-Instance Data with Bags as Distributions
Authors: Gary Doran, Soumya Ray
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support this theoretical result with an empirical evaluation using a number of synthetic and real-world MI datasets that have been annotated with instance labels. |
| Researcher Affiliation | Academia | Gary Doran and Soumya Ray Department of Electrical Engineering and Computer Science Case Western Reserve University Cleveland, OH 44106, USA {gary.doran,sray}@case.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states 'We implement KI-SVM using published code2' which refers to a third-party code. It does not explicitly state that the code for their own methodology is being released or provide a link to it. |
| Open Datasets | Yes | We use the Spatially Independent, Variable Area, and Lighting (SIVAL) dataset from the CBIR domain, which has been annotated with both bag and instance labels (Settles, Craven, and Ray 2008). |
| Dataset Splits | Yes | We evaluate algorithms using 10-fold cross-validation, with 5-fold inner-validation used to select parameters using random search (Bergstra and Bengio 2012). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Python with Num Py and CVXOPT libraries but does not provide specific version numbers for these software components, which is required for reproducibility. |
| Experiment Setup | Yes | We use the radial basis function (RBF) kernel in all cases, with scale parameter γ [10 6, 101], and regularization loss trade-off parameter C [10 2, 105]. |