Support Vector Machine 편집하기 (부분)

==Advantages and Disadvantages of SVM==
'''Advantages:'''
*'''Effective in High-Dimensional Spaces''': SVM performs well with high-dimensional data, especially when the number of features exceeds the number of samples.
*'''Robust with Clear Margins''': Provides strong predictive power with a distinct margin, making it effective for binary classification.
'''Disadvantages:'''
*'''Computationally Intensive''': SVM can be slow to train with large datasets, especially with complex kernels.
*'''Less Effective on Noisy Data''': Sensitive to overlapping classes or mislabeled data, which can reduce its classification accuracy.