Learning in Reproducing Kernel Kreı̆n Spaces
Authors: Dino Oglic, Thomas Gaertner
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The approach is evaluated empirically using indefinite kernels defined on structured as well as vectorial data. The empirical results demonstrate a superior performance of our approach over the state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1School of Computer Science, University of Nottingham, UK 2Institut f ur Informatik III, Universit at Bonn, Germany. |
| Pseudocode | No | The paper describes algorithms and derivations mathematically but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing its source code or links to a code repository. |
| Open Datasets | Yes | All datasets have been downloaded from the LIBSVM library (Chang & Lin, 2011). ... using a set of benchmark datasets for learning with indefinite kernels (Duin & Pekalska, 2009). |
| Dataset Splits | Yes | We measure the effectiveness of a baseline/method using the average root mean squared error, computed after performing 10 fold outer cross-validation. ... 10 fold stratified cross-validation. |
| Hardware Specification | No | The paper mentions 'University of Nottingham High Performance Computing Facility' in the acknowledgements, but it does not specify any particular hardware details such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions 'L-BFGS-B minimization procedure' and 'LIBSVM library (Chang & Lin, 2011)' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In Section 3.3, we derive the gradient of an optimal solution to the risk minimization problem with respect to the hyperparameters of the model (e.g., the regularization parameters, hypersphere radius, and/or kernel-specific parameters). ... A detailed description of the experimental setup can be found in Appendix C. |