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CS44 Final Project Report by Qi Gu, Zhifei Song Image Classification Using SVM, KNN and Performance Comparison with Logistic Regression Team member: Qi Gu, Zhifei Song Abstract: Image classification.

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  2. Begin by entering your personal information in the designated fields. Ensure all details are accurate and comply with the required format.
  3. Provide details regarding the algorithms being compared. Fill in the sections for SVM (Support Vector Machine) and KNN (K-Nearest Neighbors) with relevant performance metrics, ensuring clarity in each entry.
  4. In the next section, outline the dataset used for testing and training. Specify the number of images and their categories, and describe how they were preprocessed for analysis.
  5. Review the experiment results. Include quantitative analyses such as accuracy rates for both algorithms. Clearly present the findings in a format that is easy to read.
  6. Conclude with an analysis comparing the performance of SVM, KNN, and Logistic Regression in your study, ensuring to reference any metrics or findings that support your conclusions.
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SVM and kNN exemplify several important trade-offs in machine learning (ML). SVM is less computationally demanding than kNN and is easier to interpret but can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns but its output is more challenging to interpret.

SVM has an accuracy value around 97.1%, while KNN has an accuracy value around 88.5%. And also, SVM has processing time faster than KNN.

Advantages of SVM Classifier: SVM works relatively well when there is a clear margin of separation between classes. SVM is more effective in high dimensional spaces and is relatively memory efficient. SVM is effective in cases where the dimensions are greater than the number of samples.

The SVM is extremely fast, classifying 12 megapixel aerial images in roughly ten seconds as opposed to the kNN which takes anywhere from forty to fifty seconds to classify the same image.

Advantages. SVM Classifiers offer good accuracy and perform faster prediction compared to Naïve Bayes algorithm. They also use less memory because they use a subset of training points in the decision phase. SVM works well with a clear margin of separation and with high dimensional space.

With their ability to generalize well in high dimensional feature spaces, SVMs eliminate the need for feature selection, making the ap- plication of text categorization considerably easier. Another advantage of SVMs over the conventional methods is their robustness.

Decision tree vs SVM : SVM uses kernel trick to solve non-linear problems whereas decision trees derive hyper-rectangles in input space to solve the problem. Decision trees are better for categorical data and it deals colinearity better than SVM.

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Form Packages
Adoption
Bankruptcy
Contractors
Divorce
Home Sales
Employment
Identity Theft
Incorporation
Landlord Tenant
Living Trust
Name Change
Personal Planning
Small Business
Wills & Estates
Packages A-Z
Form Categories
Affidavits
Bankruptcy
Bill of Sale
Corporate - LLC
Divorce
Employment
Identity Theft
Internet Technology
Landlord Tenant
Living Wills
Name Change
Power of Attorney
Real Estate
Small Estates
Wills
All Forms
Forms A-Z
Form Library
Customer Service
Terms of Service
Privacy Notice
Legal Hub
Content Takedown Policy
Bug Bounty Program
About Us
Blog
Affiliates
Contact Us
Delete My Account
Site Map
Industries
Forms in Spanish
Localized Forms
State-specific Forms
Forms Kit
Legal Guides
Real Estate Handbook
All Guides
Prepared for You
Notarize
Incorporation services
Our Customers
For Consumers
For Small Business
For Attorneys
Our Sites
US Legal Forms
USLegal
FormsPass
pdfFiller
signNow
airSlate WorkFlow
DocHub
Instapage
Social Media
Call us now toll free:
+1 833 426 79 33
As seen in:
  • USA Today logo picture
  • CBC News logo picture
  • LA Times logo picture
  • The Washington Post logo picture
  • AP logo picture
  • Forbes logo picture
© Copyright 1997-2025
airSlate Legal Forms, Inc.
3720 Flowood Dr, Flowood, Mississippi 39232