Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel
Authors: Jia He, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets demonstrate that our method has superior performance. In this section, we evaluate our proposed model (M3LAK) on various classification tasks. |
| Researcher Affiliation | Academia | Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China 2Lab of Parallel Software and Computational Science, Institute of Software, CAS, Beijing, China 3Research Center for Brain-Inspired Intelligence, Institute of Automation, CAS, Beijing, China 4University of Chinese Academy of Sciences, Beijing 100049, China |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It only links to the public implementation of a baseline model (BEMKL). |
| Open Datasets | Yes | The Flickr dataset contains 3,411 images of 13 animals [Chen et al., 2012]. For each image, two types of features are extracted, including 634-dim real-valued features and 500-dim bag of word SIFT features. Trecvid contains 1,078 manually labeled video shots that belongs to five categories [Chen et al., 2012]. |
| Dataset Splits | Yes | In M3LAK, we perform 5-fold cross-validation on training set to decide the regularization parameter C from the integer set {5, 6, 7, 8, 9} for each data set. ... On each data set, we conduct 10-fold cross validation for all the algorithms, where nine folds of the data are used for training while the rest for testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment for their own proposed method. |
| Experiment Setup | Yes | In M3LAK, we perform 5-fold cross-validation on training set to decide the regularization parameter C from the integer set {5, 6, 7, 8, 9} for each data set. The rest parameters are fixed as follows for all data sets, i.e., m = 20, M = 100, η = 1e+3, α = 1, ar = 1e-1, aτ = v = 1e-2, bτ = br = 1e-5. |