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Explain with suitable examples Linearly and Non-linearly separable pattern classification
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Linear Non-Linear
Algorithms does not require initial values Algorithms require initial values
Globally concave; Non convergence is not an issue Non convergence is a common issue
Normally solved using direct methods Usually an iterative process
Solutions is unique Multiple minima in the sum of squares
Yields unbiased estimates even when errors are uncorrelated with predictor values Yields biased estimates
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