Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
Authors: Ricardo Henao, Xin Yuan, Lawrence Carin
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive set of experiments demonstrate the utility of using a nonlinear Bayesian SVM within discriminative feature learning and factor modeling, from the standpoints of accuracy and interpretability. |
| Researcher Affiliation | Academia | Ricardo Henao, Xin Yuan and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 {r.henao,xin.yuan,lcarin}@duke.edu |
| Pseudocode | No | The paper describes algorithms and inference procedures in detail using prose and mathematical equations but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper states 'All code used in the experiments was written in Matlab...' but does not provide any explicit statement about making the code open-source or offer a link to a code repository. |
| Open Datasets | Yes | We first compare the performance of the proposed Bayesian hierarchy for nonlinear SVM (BSVM) against EP-based GP classification (GPC) and an optimization-based SVM, on six well known benchmark datasets. (...) USPS handwritten digits dataset, consisting of 1540 gray scale 16 16 images (...) The dataset originally introduced in [24] consists of gene expression measurements from primary breast tumor samples... |
| Dataset Splits | Yes | The parameters of the SVM {γ, θ} are obtained by grid search using an internal 5-fold cross-validation. (...) validation is done by 10-fold cross-validation. |
| Hardware Specification | Yes | All code used in the experiments was written in Matlab and executed on a 2.8GHz workstation with 4Gb RAM. |
| Software Dependencies | No | The paper states that the code was written in 'Matlab' but does not specify any version numbers for Matlab or for any other software libraries or dependencies used. |
| Experiment Setup | Yes | In all experiments we set the covariance function to (i) either the square exponential (SE)... or (ii) the automatic relevance determination (ARD) SE... (...) The parameters of the SVM {γ, θ} are obtained by grid search using an internal 5-fold cross-validation. (...) For our model we set 200 as the maximum number of iterations of the ECM algorithm and run ML-II every 20 iterations. (...) For inference, we set K = 10, a SE covariance function and run the sampler for 1200 iterations, from which we discard the first 600 and keep every 10-th for posterior summaries. |