Transfer Learning with Active Queries from Source Domain
Authors: Sheng-Jun Huang, Songcan Chen
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed method is validated by experiments on 15 datasets for sentiment analysis and text categorization. |
| Researcher Affiliation | Academia | Sheng-Jun Huang and Songcan Chen College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 211106 {huangsj, s.chen}@nuaa.edu.cn |
| Pseudocode | Yes | Algorithm 1 The TLAS Algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code release. |
| Open Datasets | Yes | The Sentiment Analysis dataset1 contains product reviews on Amazon from four domains: Book, DVD, Electronics and Kitchen. For each domain, 1000 positive reviews and 1000 negative reviews are collected. Each review text is represented by a 200 dimensional feature vector according to [Chattopadhyay et al., 2013]. ... For the text categorization task, we use a preprocessed subset of Reuters-215782 as in [Dai et al., 2007]. 1http://www.cs.jhu.edu/ mdredze/datasets/sentiment 2http://www.cse.ust.hk/TL/dataset/Reuters.zip |
| Dataset Splits | Yes | For each dataset, we randomly divide the source domain data into two parts: 10% as the labeled set SL, and the rest 90% as the unlabeled set SU. Similarly, the target domain data is randomly divided into three parts: 50% for testing, 10% as the labeled set TL, and the rest 40% as the unlabeled set TU. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. |
| Software Dependencies | No | We employ Lib SVM [Chang and Lin, 2011] with default parameters to implement the classification model. (No version number for Lib SVM is provided). |
| Experiment Setup | Yes | In our experiments, we set n Q = 10 and λ = 10 as default for all datasets, and compute the kernel matrix K using RBF kernel with default parameters. |