Introduction to support vector machines and other kernel-based learning methods

SVM with Examples\Linear Kernel\confusionMatrix.m
.................\.............\data1.csv
.................\.............\data2.csv
.................\.............\grad_asc_alpha.m
.................\.............\holdout.m
.................\.............\linear1.m
.................\.............\linear2.m
.................\.............\svm_data1.mlx
.................\.............\svm_data2.mlx
.................\Polynomial Kernel\confusionMatrix.m
.................\.................\d3.fig
.................\.................\d3i.fig
.................\.................\d4.fig
.................\.................\data3Former.csv
.................\.................\data4.csv
.................\.................\grad_asc_Nonlinear.m
.................\.................\holdout.m
.................\.................\polyData3.m
.................\.................\polyData4.m
.................\.................\svm_polyData3.mlx
.................\.................\svm_polyData4.mlx
.................\Solving XOR Problem using SVM\confusionMatrix.m
.................\.............................\grad_asc_Nonlinear.m
.................\.............................\holdout.m
.................\.............................\xor.fig
.................\.............................\XOR.mlx
.................\.............................\xor_i.fig
.................\.............................\X_OR.m
.................\.VM_Noisy Data\confusionMatrix.m
.................\..............\d5.fig
.................\..............\data5.csv
.................\..............\grad_asc_Nonlinear.m
.................\..............\holdout.m
.................\..............\svmData5.m
.................\..............\svm_Data5.mlx
license.txt

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