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Enhancing Multi-Class Classification with Efficient KMOD Kernel SVM

Learn to implement a multi-class classifier with low error rate and improved generalization using KMOD Kernel-based SVM. Explore Mercer's Theorem, kernel functions, and achieve excellent results.

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Enhancing Multi-Class Classification with Efficient KMOD Kernel SVM

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  1. • Objectives: A Multi-Class Classifier Low Error Rate Generalization Resources: Ayat – KMOD SVM kernel Burges - SVM Tutorial Collobert - SVMTorch ECE 8443 – Pattern Recognition LECTURE 40: A MULTI-CLAS CLSSIFIER USING KMOD KERNEL BASED SVM • URL: .../publications/courses/ece_8443/lectures/2004_spring/lecture_40_01.ppt

  2. 40: KMOD KERNEL BASED SVM MERCER’S THEOREM • The kernel matrix is Symmetric Positive Definite • Any symmetric positive definite matrix can be regarded as a kernel matrix, that is as an inner product matrix in some space

  3. 40: KMOD KERNEL BASED SVM MERCER’S CONDITION Every function k(x,y) that satisfies Mercer’s condition may be considered as an eligible kernel. Mercer’s condition is stated as: is finite, so that then

  4. 40: KMOD KERNEL BASED SVM KERNEL FUNCTIONS • Common Kernel Functions: Kernel Formula Linear Polynomial RBF Exponential RBF Sigmoid KMOD

  5. 40: KMOD KERNEL BASED SVM RESULTS Results:

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