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..
Outlier Path: A Homotopy Algorithm for Robust SVM
Authors: Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama, Ichiro Takeuchi
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compared the proposed outlier-path (OP) algorithm with the concave-convex procedure (CCCP) (Yuille & Rangarajan, 2002). In most of the existing RSVM studies, CCCP or a variant called difference of convex (DC) programming are used for optimizing RSVM (Shen et al., 2003; Krause & Singer, 2004; Liu et al., 2005; Liu & Shen, 2006; Collobert et al., 2006; Wu & Liu, 2007). We used the 10 benchmark data sets listed in Table 1. We randomly divided each data set into the training (40%), validation (30%), and test (30%) sets for training, model selection (including the selection of θ or s), and performance evaluation, respectively. |
| Researcher Affiliation | Academia | Shinya SUZUMURA EMAIL Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi 466 8555 Japan Kohei OGAWA EMAIL Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi 466 8555 Japan Masashi Sugiyama EMAIL Tokyo Institute of Technology O-okayama, Meguro-ku, Tokyo 152-8552, Japan Ichiro Takeuchi EMAIL Nagoya Institute of Technology Gokiso-cho, Showa-ku, Nagoya, Aichi 466 8555 Japan |
| Pseudocode | Yes | Algorithm 1 Outlier Path Algorithm Algorithm 2 Continuous Step (C-step) Algorithm 3 Discontinuous Step (D-step) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We used the 10 benchmark data sets listed in Table 1. ... Table 1. Benchmark data sets. n and d denote the number of instances and the input dimensionality, respectively. Data D1 Breast Cancer Diagnostic ... D2 Australian Credit Approval ... D5 Spambase ... D7 Gisette ... |
| Dataset Splits | Yes | We randomly divided each data set into the training (40%), validation (30%), and test (30%) sets for training, model selection (including the selection of θ or s), and performance evaluation, respectively. |
| Hardware Specification | No | The paper discusses computational time and presents results in Figure 5, but it does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or solvers used in their implementation. |
| Experiment Setup | Yes | In all the setups, the regularization parameter C was chosen from {0.01, 0.1, 1, 10, 100}, while the candidates of the homotopy parameter θ or s were set as follows: In OP-θ, all the break-points θBP were considered as the candidates (note that the local solutions at each break-point have been already computed in the homotopy computation). In OP-s, all the break-points for s BP between sinit := mini Nn yi f(xi) and 0 are considered as the candidates. |