Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improving the Generalization Performance of Multi-class SVM via Angular Regularization
Authors: Jianxin Li, Haoyi Zhou, Pengtao Xie, Yingchun Zhang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On various datasets, we demonstrate the efficacy of the regularizer in reducing overfitting. Table 1 shows the classification accuracy of ℓ2-regularized MSVM on several datasets, where the gap between training and testing accuracy are still substantial. In this paper, we study a new type of regularizer that encourages the coefficient vectors (equivalently, the hyperplanes parameterized by them) in MSVM to have large angles, for the purpose to control overfitting. Fig. 1 illustrates the idea. Section 4 Experiments In this section, we present experimental results. We evaluated our method on ten datasets. Table 2 summaries their statistics. Table 3 shows the classification results on six datasets. |
| Researcher Affiliation | Collaboration | Jianxin Li1, Haoyi Zhou1, Pengtao Xie2,3, Yingchun Zhang1 1 School of Computer Science and Engineering, Beihang University 2 Machine Learning Department, Carnegie Mellon University 3 Petuum Inc, USA EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms and methods (e.g., stochastic sub-gradient method) but does not provide a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code or a link to a code repository. |
| Open Datasets | Yes | Table 2: Statistics of Datasets. Dataset #Classes #Train #Test #Features Yale B 38 1500 914 1024 Image Net-50 50 30K 10K 128 Covtype 7 100K 40K 54 Shuttle 7 30450 14500 9 New-thyroid 3 108 107 5 Yeast 10 1134 350 8 Dermatology 6 323 35 33 Page-Blocks 5 4924 548 10 Wine-Quality-Red 6 1439 160 11 Zoo 7 89 12 16 |
| Dataset Splits | Yes | The regularization parameters λ and β are tuned in the range [2−20, 2−19, . . . , 220] via 5-fold cross validations. All the experiments are conducted over 10 random train/test splits and the results are averaged over the 10 runs. |
| Hardware Specification | Yes | The algorithms were implemented in MATLAB and the experiments were run on a Linux machine with a 2.00GHz Xeon CPU and 256G memory. |
| Software Dependencies | No | The paper mentions "implemented in MATLAB" but does not specify a version number for MATLAB or any other libraries or software dependencies with their versions. |
| Experiment Setup | Yes | The regularization parameters λ and β are tuned in the range [2−20, 2−19, . . . , 220] via 5-fold cross validations. In the stochastic sub-gradient descent algorithm, the mini-batch size and number of epochs are set to 20 and 50 respectively. The learning rate is set according to ADADELTA [Zeiler, 2012] in all methods. |