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What is Feature Extraction in Pattern Recognition about?

Pattern recognition involves feature extraction to convert raw patterns into feature vectors, then classification of those vectors. Nearest neighbor classification assigns patterns to the class of the closest training pattern. K nearest neighbors takes a majority vote of the K closest training patterns. Unsupervised learning uses clustering to group similar patterns without labeled classes. K-means clustering assigns patterns to K cluster means, then recalculates the means, repeating until convergence.

Author

ANiket

Language

EN