Memory Efficient Kernel Approximation
Authors: Si Si, Cho-Jui Hsieh, Inderjit Dhillon
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically demonstrate the benefits of our proposed method, MEKA on various data sets. |
| Researcher Affiliation | Academia | Si Si SSI@CS.UTEXAS.EDU Cho-Jui Hsieh CJHSIEH@CS.UTEXAS.EDU Inderjit S. Dhillon INDERJIT@CS.UTEXAS.EDU Department of Computer Science, The University of Texas, Austin, TX 78721, USA |
| Pseudocode | Yes | Algorithm 1: Memory Efficient Kernel Approximation (MEKA) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | All the datasets are downloaded from www.csie.ntu. edu.tw/ cjlin/libsvmtools/datasets and UCI repository(Bache & Lichman, 2013). |
| Dataset Splits | Yes | The parameters are chosen by five fold cross-validation and shown in Table 3. |
| Hardware Specification | Yes | All the experiments are conducted on a machine with an Intel Xeon X5440 2.83GHz CPU and 32G RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names). |
| Experiment Setup | Yes | The parameters are chosen by five fold cross-validation and shown in Table 3. |