Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Instance Concepts from Multiple-Instance Data with Bags as Distributions
Authors: Gary Doran, Soumya Ray
AAAI 2014 | Venue PDF | 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 EMAIL |
| 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]. |