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CS44 Final Project Report by QI GU, China Song Image Classification Using SVM, CNN and Performance Comparison with Logistic Regression Team member: QI GU, China Song Abstract: Image classification

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What is performance comparison of svm?

Performance comparison of svm refers to the evaluation and analysis of the performance of SVM (Support Vector Machine) algorithm in comparison to other classification algorithms. It involves assessing various metrics such as accuracy, precision, recall, F1-score, and training/testing time to determine the effectiveness and efficiency of SVM.

Who is required to file performance comparison of svm?

The individuals or research organizations conducting experiments or studies on SVM and other classification algorithms are usually responsible for filing the performance comparison of svm. It is typically done by researchers, data scientists, or machine learning practitioners to document and share their findings.

How to fill out performance comparison of svm?

To fill out the performance comparison of SVM, one needs to collect experimental data by implementing SVM and other classification algorithms on benchmark datasets or real-world datasets. Then, various performance metrics like accuracy, precision, recall, F1-score, and training/testing time should be computed and compared. The results can be summarized in tables, charts, or graphs to present the findings clearly.

What is the purpose of performance comparison of svm?

The main purpose of performance comparison of SVM is to evaluate and compare the effectiveness and efficiency of SVM algorithm against other classification algorithms. By conducting such comparisons, researchers and practitioners can identify the strengths and weaknesses of SVM, understand the scenarios where SVM performs well or poorly, and make informed decisions regarding algorithm selection for specific machine learning tasks.

What information must be reported on performance comparison of svm?

The performance comparison of SVM typically includes reporting various metrics such as accuracy, precision, recall, F1-score, and training/testing time for both SVM and other classification algorithms. Additionally, details about the dataset(s) used, experimental setup, parameter configurations, and statistical significance tests (if applicable) should be provided. Any challenges faced, limitations, or recommendations for further improvements can also be mentioned.