The training set has 3 unique label values (A, B, C). Assume that you have 3 trained binary classifiers (M1, M2, M3) using logistic regression. M1 classifies an input to A or B; M2 classifies an input to B or C and M3 classifies an input to A or C. How can you combine the 3 classifiers to one multinomial classifier to classify an input to A, B or C?

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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The training set has 3 unique label values (A, B, C). Assume
that you have 3 trained binary classifiers (M1, M2, M3) using logistic
regression. M1 classifies an input to A or B; M2 classifies an input to B
or C and M3 classifies an input to A or C. How can you combine the 3
classifiers to one multinomial classifier to classify an input to A, B or
C?
Compare L1 norm (Manhattan distance) and L2
norm(Euclidean distance). At least 3 differences besides the
definition.
Transcribed Image Text:The training set has 3 unique label values (A, B, C). Assume that you have 3 trained binary classifiers (M1, M2, M3) using logistic regression. M1 classifies an input to A or B; M2 classifies an input to B or C and M3 classifies an input to A or C. How can you combine the 3 classifiers to one multinomial classifier to classify an input to A, B or C? Compare L1 norm (Manhattan distance) and L2 norm(Euclidean distance). At least 3 differences besides the definition.
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