Summary of An Autotuning-based Optimization Framework For Mixed-kernel Svm Classifications in Smart Pixel Datasets and Heterojunction Transistors, by Xingfu Wu and Tupendra Oli and Justin H. Qian and Valerie Taylor and Mark C. Hersam and Vinod K. Sangwan
An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors
by Xingfu Wu, Tupendra Oli, Justin H. Qian, Valerie Taylor, Mark C. Hersam, Vinod K. Sangwan
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an autotuning-based optimization framework to optimize hyperparameters in Support Vector Machines (SVMs) for high-dimensional data. The framework is applied to two SVMs with mixed-kernel combinations of Sigmoid and Gaussian kernels, used for smart pixel datasets in high-energy physics (HEP) and mixed-kernel heterojunction transistors (MKH). Results show that optimal hyperparameter selection varies greatly between applications and datasets, highlighting the importance of proper tuning for high classification accuracy. The framework effectively quantifies proper ranges for hyperparameters to achieve high accuracy rates of 94.6% for HEP and 97.2% with reduced tuning time for MKH. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the best settings for a special kind of machine learning model called Support Vector Machines (SVMs). SVMs are great at classifying things, but they can be tricky to set up just right. The authors came up with a new way to figure out the best settings for two types of SVMs that combine different kinds of data. They tested their method on real-world datasets and found that it really works! By using this method, we can make sure our SVMs are as accurate as possible. |
Keywords
» Artificial intelligence » Classification » Hyperparameter » Machine learning » Optimization » Sigmoid
