Generalized Hierarchical Kernel Learning
Authors: Pratik Jawanpuria, Jagarlapudi Saketha Nath, Ganesh Ramakrishnan
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark REL data sets illustrate the efficacy of the proposed generalizations. Keywords: multiple kernel learning, mixed-norm regularization, multi-task learning, rule ensemble learning, active set method |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai 400076, INDIA |
| Pseudocode | Yes | Algorithm 1 Active Set Algorithm Outline Algorithm 2 Mirror Descent Algorithm for solving (14) Algorithm 3 Active Set Algorithm |
| Open Source Code | Yes | The implementations of both g HKLρ and g HKLMT ρ are available at http://www.cse.iitb.ac.in/~pratik.j/ghkl. |
| Open Datasets | Yes | We report the results of simulation in REL on several benchmark binary and multiclass classification data sets from the UCI repository (Blake and Lichman, 2013). |
| Dataset Splits | Yes | For every data set, we created 10 random train-test splits with 10% train data (except for MONK-3 data set, whose train-test split of 122 432 instances respectively was already given in the UCI repository). |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions the implementation of algorithms but does not provide specific software dependencies or library versions used (e.g., Python, PyTorch, or other solvers with version numbers). |
| Experiment Setup | Yes | In each case, a three-fold cross validation procedure was employed to tune the C parameter with values in {10-3, 10-2, ..., 103}. For Rule Fit: All the parameters were set to the default values mentioned by the authors. In particular, the model was set in the mixed linear-rule mode, average tree size was set 4 and maximum number of trees were kept as 500. For SLI and ENDER: all parameters were set to their defaults. |