Robust and Efficient Kernel Hyperparameter Paths with Guarantees
Authors: Joachim Giesen, Soeren Laue, Patrick Wieschollek
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results for these problems applied to various data sets confirm the theoretical complexity analysis. |
| Researcher Affiliation | Academia | Joachim Giesen JOACHIM.GIESEN@UNI-JENA.DE S oren Laue SOEREN.LAUE@UNI-JENA.DE Patrick Wieschollek PATRICK@WIESCHOLLEK.INFO Friedrich-Schiller-Universit at Jena, Germany |
| Pseudocode | No | The algorithm is described in Section 3 in prose, but no structured pseudocode or algorithm block is provided. |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of its own source code for the described methodology. |
| Open Datasets | Yes | all data sets that have been used in our experiments were retrieved from the LIBSVM Website, see (Lin). LIBSVM Tools. Data sets available at www.csie.ntu.edu.tw/~cjlin/ libsvmtools/datasets/. |
| Dataset Splits | No | The paper mentions using a 'test data set' but does not specify train/test/validation splits (e.g., percentages or sample counts) or cross-validation details for the datasets. |
| Hardware Specification | No | The paper mentions using MATLAB as a test environment but does not specify any hardware details like CPU/GPU models, memory, or specific computing resources. |
| Software Dependencies | Yes | LIBSVM Version 3.17, whose implementation is described in (Fan et al., 2005), has been used to compute primal-dual optimal pairs. |
| Experiment Setup | Yes | The regularization parameter c was set to 0.1 in all the experiments. The regularization parameter λ was set to 0.1 in the experiments. |