Growing Adaptive Multi-hyperplane Machines
Authors: Nemanja Djuric, Zhuang Wang, Slobodan Vucetic
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments on data of varying characteristics to measure accuracy of the proposed method, and to estimate its robustness to noise. We evaluated the algorithms on 5 real-world data sets of very different sizes, dimensions, and complexities. |
| Researcher Affiliation | Collaboration | Nemanja Djuric 1 Zhuang Wang 2 Slobodan Vucetic 3 1Uber ATG, Pittsburgh, PA, USA 2Facebook, Menlo Park, CA, USA 3Temple University, Philadelphia, PA, USA. |
| Pseudocode | Yes | Algorithm 1 Training algorithm for GAMM |
| Open Source Code | Yes | The GAMM implementation is available for download at https://github.com/djurikom/Budgeted SVM. |
| Open Datasets | Yes | We evaluated the algorithms on 5 real-world data sets of very different sizes, dimensions, and complexities. The datasets include usps, letter, ijcnn1, rcv1, and mnist. A footnote links to their source: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/, last accessed June 2020. |
| Dataset Splits | No | The paper mentions 'We created 15,000 training and 5,000 test examples' for synthetic data and 'λ through cross-validation' for parameter tuning, but does not explicitly state a separate validation dataset split with specific percentages or counts. |
| Hardware Specification | Yes | To better illustrate scalability we evaluated the algorithms on the lower-end Intel R E7400 with 2.80GHz processor and 4GB RAM |
| Software Dependencies | No | The paper mentions using 'scikit-learn implementations' for some baseline models, but does not provide specific version numbers for scikit-learn or any other software dependencies. It mentions C++ as the implementation language for GAMM, and names other tools/libraries without versions. |
| Experiment Setup | Yes | We set parameters to their default values, c = 10 for AMM and c = 50 for GAMM (due to more frequent introduction of new weights), p = 0.2, β = 0.99, set λ through cross-validation, and trained the models for 15 epochs. |