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Explain with suitable examples Linearly and Non-linearly separable pattern classification
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written 8.0 years ago by |
Linear | Non-Linear |
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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 |