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Physical Sciences › Engineering › Control and Systems Engineering
The effect of gamma value on support vector machine performance with different kernels
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Identified Weaknesses
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Paper Summary
Paperzilla title
Gamma-Ray Vision? Not So Fast! A Look at SVM Kernel Performance
This paper explored the effect of the gamma parameter on SVM classifier performance using polynomial, RBF, and sigmoid kernels across five datasets. The results indicated an uneven influence of gamma on accuracy, with polynomial and sigmoid kernels showing greater sensitivity to changes in gamma than the RBF kernel. Optimal gamma values varied across datasets and kernel functions, suggesting the need for careful parameter tuning.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Lack of Control Group
The experimental design lacks a clear control group, making it difficult to isolate the effect of the gamma parameter. Without a baseline comparison, it's hard to determine if the observed changes in accuracy are solely attributable to gamma or influenced by other factors.
Limited Datasets and Gamma Values
The limited number of datasets (only five) and the specific gamma values tested (0.3, 0.6, 0.9) restrict the generalizability of the findings. A wider range of datasets and gamma values would strengthen the conclusions and provide a more comprehensive understanding of the parameter's impact.
Overfitting Concerns
The paper doesn't adequately address the potential for overfitting, especially given the small size of some datasets. Overfitting could lead to inflated accuracy estimates and misleading conclusions about the true performance of the SVM with different kernels and gamma values.
Computational Cost Not Addressed
The paper lacks a discussion of the computational cost associated with different kernels and gamma values. This information is crucial for practical applications, as some kernels and parameter settings might be computationally prohibitive for large datasets.
Rating Explanation
This study investigates the impact of the gamma parameter on SVM performance with different kernels. While the research question is relevant, the methodology suffers from several limitations, including a lack of a control group, limited datasets and gamma values, overfitting concerns, and a lack of discussion about computational cost. These limitations restrict the generalizability and impact of the findings, resulting in an average rating.
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Original Title:
The effect of gamma value on support vector machine performance with different kernels
File Name:
14283.pdf
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July 14, 2025 at 10:35 AM
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