Explain on PCA , SVM and image processing concepts

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Accepted Answer

PCA is a dimensionality reduction technique, SVM is a machine learning algorithm, and image processing involves manipulating images.

PCA (Principal Component Analysis) is used to reduce the dimensionality of data by finding the most important features.
SVM (Support Vector Machine) is a supervised machine learning algorithm used for classification and regression tasks.
Image processing involves techniques like filtering, segmentation, and feature extraction to manipulate and analyze images.
Example: PCA can be used to reduce the number of features in a dataset before applying SVM for classification.
Example: Image processing techniques can be used to enhance the quality of medical images for diagnosis.

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