Fredholm Multiple Kernel Learning for Semi-Supervised Domain Adaptation
Authors: Wei Wang, Hao Wang, Chen Zhang, Yang Gao
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive empirical studies verify the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Wei Wang,1 Hao Wang,1,2 Chen Zhang,1 Yang Gao1 1. Science and Technology on Integrated Information System Laboratory 2. State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences, Beijing 100190, China wangwei2014@iscas.ac.cn |
| Pseudocode | Yes | Algorithm 1 Transfer Fredholm Multiple Kernel Learning |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Amazon, DSLR, Webcam and Caltech256 are four benchmark databases widely used for visual domain adaptation evaluation (Gong et al. 2012). |
| Dataset Splits | Yes | Following (Hoffman et al. 2014): the source data contains 20 examples per class randomly selected from Amazon source (8 from other source domains); the labeled target data contains 3 labeled examples per class from target domain. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions software components and methods like SVM, Fred-st, KMM, GFK, MMDT, and DTMKL-f, and types of kernels, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For SVM-st, SVM-t, DTMKL-f and KMM, we choose C {0.001, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100}. For Fred-st, Fred-t and TFMKL, β is searched in the range [10 7, 101]. For DTMKL-f, we search θ, λ and ζ in the range [10 2, 102]. The parameters λs, λt and θ in TFMKL are searched in the range [10 1, 101]. |