Online Classification Using a Voted RDA Method
Authors: Tianbing Xu, Jianfeng Gao, Lin Xiao, Amelia Regan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We examine the method using ℓ1-regularization on a large-scale natural language processing task, and obtained state-of-the-art classification performance with fairly sparse models. Table 1: Comparing performance of different algorithms. Figure 1: Different sparse feature structure by different regularization λ for v RDA with hinge and log losses. Figure 2: Trade off between the model sparsity and classification accuracy for v RDA with hinge and log losses. |
| Researcher Affiliation | Collaboration | Tianbing Xu Computer Science University of California, Irvine Jianfeng Gao, Lin Xiao Microsoft Research Redmond, WA Amelia C. Regan Computer Science University of California, Irvine |
| Pseudocode | Yes | Algorithm 1 The voted RDA method (training) Algorithm 2 The voted RDA method (testing) |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We trained the predictor on Sections 2-19 of the Penn Treebank (Marcus, Santorini, and Marcinkiewicz 1993) |
| Dataset Splits | Yes | We used Section 20-21 to optimize training parameters, including the regularization weight λ and the learning rate η, and then evaluated the predictors on Section 22. The training set contains 36K sentences, while the development set and the test set have 4K and 1.7K, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | We used η = 0.05 and λ = 1e 5 for hinge loss, and η = 1000 and λ = 1e 4 for log loss. |