Soft Margin Consistency Based Scalable Multi-View Maximum Entropy Discrimination
Authors: Liang Mao, Shiliang Sun
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of SMVMED on multiple real-world datasets and get encouraging results. |
| Researcher Affiliation | Academia | Liang Mao, Shiliang Sun Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China lmao14@outlook.com, slsun@cs.ecnu.edu.cn |
| Pseudocode | No | The paper describes the SMO algorithm in text and equations, but it does not contain structured pseudocode or algorithm blocks clearly labeled as such. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | Course: The dataset is the web-page dataset used in the co-training experiment [Blum and Mitchell, 1998]. Ads: The dataset consists of 459 ads images and 2820 non-ads images [Kushmerick, 1999]. The dataset is the UJIIndoor Loc indoor localization database [Joaquın et al., 2014]. |
| Dataset Splits | Yes | We randomly select half of the dataset as the training set, and the rest is divided into the validation set and the test set equally. |
| Hardware Specification | Yes | All of the experiments are executed on an Intel(R) Core(TM) i7-3667U 2.00GHz CPU with 8GB of RAM using Matlab R2014a. |
| Software Dependencies | Yes | All of the experiments are executed on an Intel(R) Core(TM) i7-3667U 2.00GHz CPU with 8GB of RAM using Matlab R2014a. |
| Experiment Setup | Yes | For prediction functions, besides using two views sign(f1) and sign(f2) separately, the hybrid prediction function is also taken into consideration... Parameter c in SMVMED, MVMED and AMVMED is independently chosen from {21, 22, . . . , 25} for Course, and from {21, 22, . . . , 215} for Ads. Parameter in SMVMED is chosen from {0, 0.1, . . . , 1.0}. The linear kernel is used in all the experiments. Since the performance of SMVMED is not sensitive to parameter c on the dataset, we fix it to 1. |