Diversifying Convex Transductive Experimental Design for Active Learning
Authors: Lei Shi, Yi-Dong Shen
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on several benchmark data sets demonstrate that Diversified CTED significantly improves CTED and consistently outperforms the state-of-the-art methods, verifying the effectiveness and advantages of incorporating the proposed diversity regularizer into CTED.6 Experiments Following a same experimental protocol in [Yu et al., 2008], we perform classification experiments on five benchmark data sets to demonstrate the effectiveness of the proposed method (i.e., Diversified CTED) and give analysis on the experimental results. |
| Researcher Affiliation | Academia | Lei Shi1,2 and Yi-Dong Shen1 1State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences 2University of Chinese Academy of Sciences, Beijing 100190, China |
| Pseudocode | Yes | Algorithm 1 The Optimization Algorithm for Diversified CTED |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository. |
| Open Datasets | Yes | We conduct the experiments on 5 publicly available data sets, including 2 digit recognition data sets (i.e., USPS [Wu and Sch olkopf, 2006] and MNIST [Liu et al., 2010]), 2 text data sets (i.e., Web KB [Wang et al., 2011] and Newsgroup [Yu et al., 2005]) and one face data set (i.e., ORL [Cai et al., 2006]). |
| Dataset Splits | No | The paper describes a training and testing split (50% for candidate set, 50% for prediction) but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using SVM and KNN classifiers but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | To fairly compare the above algorithms, we tune the parameters for all these methods from a large range of {10 3, 10 2, ..., 103}. To illustrate the effects of , we fix the number of selected samples as 50. For CTED, we report the best performance it achieves. For our method, i.e., DCTED, we vary the value of in [10 3, 10 2, ..., 103] and report the corresponding results. |