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Topics To Learn In Support Vector Machine.

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Topics To Learn In SVM

Table Of Contents:

  1. Introduction to SVM
    • What is SVM?
    • Use cases and applications of SVM
    • Strengths and weaknesses of SVM
  2. Mathematical Foundations
    • Linear separability
    • Concept of a hyperplane
    • Margin and margin maximization
    • Support vectors and their role
    • Functional and geometric margins
  3. SVM for Linearly Separable Data
    • Objective function for linear SVM
    • Hard margin SVM
    • Optimization problem formulation
    • Lagrange multipliers and the dual problem
  4. SVM for Non-Linearly Separable Data
    • Soft margin SVM
    • Slack variables and their significance
    • Trade-off parameter CC: bias-variance tradeoff
    • Practical scenarios for soft margin SVM
  5. Kernel Trick
    • What is the kernel trick?
    • Common types of kernels:
      • Linear kernel
      • Polynomial kernel
      • Radial Basis Function (RBF) kernel (Gaussian kernel)
      • Sigmoid kernel
    • Choosing the right kernel
    • Custom kernel functions
  6. SVM Optimization
    • Primal vs. dual formulation
    • Quadratic programming (QP) problem
    • Sequential Minimal Optimization (SMO) algorithm
    • Convergence criteria for optimization
  7. Multiclass SVM
    • One-vs-One approach
    • One-vs-Rest approach
    • Challenges in multiclass classification with SVM
  8. SVM in Practice
    • Preprocessing for SVM:
      • Feature scaling
      • Handling imbalanced datasets
    • Hyperparameter tuning:
      • Regularization parameter (CC)
      • Kernel parameters (e.g., γ\gamma in RBF)
    • Cross-validation for SVM models
  9. SVM for Regression (SVR)
    • Difference between SVM and SVR
    • Epsilon-insensitive loss function
    • Tuning hyperparameters in SVR
  10. Advanced Topics
    • SVM with large datasets (scalability issues)
    • Online learning with SVM
    • Incremental SVM
    • Sparse SVM
    • SVM with imbalanced data (e.g., weighted SVM)
  11. SVM Implementation
    • Libraries for SVM:
      • Scikit-learn (Python)
      • LIBSVM
      • OpenCV (for image-related SVM tasks)
    • Building an SVM model in Python
    • Visualizing SVM decision boundaries
    • Comparing SVM with other classifiers (e.g., Logistic Regression, Neural Networks)
  12. Applications of SVM
    • Image classification
    • Text classification (e.g., spam detection)
    • Bioinformatics (e.g., cancer classification)
    • Time-series forecasting (using SVR)
  13. SVM in Research
    • Extensions of SVM (e.g., Twin SVM, Least-Squares SVM)
    • Hybrid models combining SVM with other techniques (e.g., SVM with PCA)
    • SVM in deep learning frameworks

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