The Coherent Loss Function for Classification
Authors: Wenzhuo Yang, Melvyn Sim, Huan Xu
ICML 2014 | Conference 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 A0096049@NUS.EDU.SG Department of Mechanical Engineering, National University of Singapore, Singapore 117576 Melvyn Sim DSCSIMM@NUS.EDU.SG Department of Decision Sciences, National University of Singapore, Singapore 117576 Huan Xu MPEXUH@NUS.EDU.SG 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. |