The Coherent Loss Function for Classification
Authors: Wenzhuo Yang, Melvyn Sim, Huan Xu
ICML 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 reports the experimental results which show that our classification method outperforms the standard SVM when additional constraints are imposed on the decision function. |
| Researcher Affiliation | Academia | Wenzhuo Yang EMAIL Department of Mechanical Engineering, National University of Singapore, Singapore 117576 Melvyn Sim EMAIL Department of Decision Sciences, National University of Singapore, Singapore 117576 Huan Xu EMAIL Department of Mechanical Engineering, National University of Singapore, Singapore 117576 |
| Pseudocode | No | The paper describes mathematical formulations and theorems but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | Yes | Three binary-class datasets Breast cancer , Ionosphere and Diabetes , and two multi-class datasets Wine and Iris from UCI (Asuncion & Newman, 2007) are used, where we randomly pick 50% as training samples, 20% as validation samples, and the rest as testing samples. |
| Dataset Splits | Yes | Three binary-class datasets Breast cancer , Ionosphere and Diabetes , and two multi-class datasets Wine and Iris from UCI (Asuncion & Newman, 2007) are used, where we randomly pick 50% as training samples, 20% as validation samples, and the rest as testing samples. |
| Hardware Specification | No | The paper states: 'To solve the resulting optimization problems, we use CVX (Grant & Boyd, 2011; 2008), and Gurobi (Gurobi Optimization, 2013) as the solver.' but provides no specific hardware details used for the experiments. |
| Software Dependencies | Yes | To solve the resulting optimization problems, we use CVX (Grant & Boyd, 2011; 2008), and Gurobi (Gurobi Optimization, 2013) as the solver. |
| Experiment Setup | Yes | For the cumulative loss formulation approach, parameter C is determined by cross-validation. For the coherent loss formulation approach, parameter a is determined by cross-validation. For each T, we repeated the experiments 20 times and computed the average classification errors. To solve the resulting optimization problems, we use CVX (Grant & Boyd, 2011; 2008), and Gurobi (Gurobi Optimization, 2013) as the solver. |