Supervized Machine Learning consists in extracting knowledge
from a set of n input examples x1,y, xn characterized by i features
a1, . . . ,ai AA, including numerical or nominal values,where each
instance has associated a desired output yj and the aim is to learn a
system capable of predicting this output for a new unseen example
in a reasonable way(with good generalization ability).
This output can be a continuous value yj AR or a classl abel yj AC (considering
an m classproblem C¼ fc1, . . . ,cmg). In the former case, it is a
regression problem, while in the latter it is a classification problem [22]. In classification,the system generated by the learning algorithm is a mapping function defined over the patterns Ai-C and it is called a classifier.
We
use SMO [58] training algorithm to obtain the SVM base
classifiers.
Supervized Machine Learning consists in extracting knowledgefrom a set of n input examples x1,y, xn characterized by i featuresa1, . . . ,ai AA, including numerical or nominal values,where eachinstance has associated a desired output yj and the aim is to learn asystem capable of predicting this output for a new unseen examplein a reasonable way(with good generalization ability).This output can be a continuous value yj AR or a classl abel yj AC (consideringan m classproblem C¼ fc1, . . . ,cmg). In the former case, it is aregression problem, while in the latter it is a classification problem [22]. In classification,the system generated by the learning algorithm is a mapping function defined over the patterns Ai-C and it is called a classifier.Weuse SMO [58] training algorithm to obtain the SVM baseclassifiers.
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