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Multi-Class Classification Methods Overview

Learn about different cases in multi-class classification scenarios, including separable classes and hyperplane distinctions. Understand the requirements and conditions for using multiple Perceptrons to classify distinct patterns. Explore the concepts of separability under various conditions.

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Multi-Class Classification Methods Overview

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  1. Multi-class Classification Mu-Chun Su

  2. Case I • Each pattern class is separable from the other classes by a single hyperplane. • M classes need M Perceptrons. • A decision can not be made if • (1) more than one perceptron have positive outputs; • (2) none of the perceptron has positiveb output.

  3. Case I

  4. Case II • Each pattern is separable from every other individual class by a distinct hyperplane. • M classes need M(M-1)/2 perceptrons. • If x belongs to wi then • These hyperplanes have the property that

  5. Case II

  6. Case III • There exist M hyperplanes with the property that if a pattern x belongs to the class wi. • If the classes are separable under case 3 condition, they are automatically separable under Case 2. • There exists no indeterminate region.

  7. Case III

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