APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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linear classifier
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non Linear Classifier
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none of the above
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None of the above
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Detailed explanation-1: -The perceptron is a linear classifier, therefore it will never get to the state with all the input vectors classified correctly if the training set D is not linearly separable, i.e. if the positive examples cannot be separated from the negative examples by a hyperplane.
Detailed explanation-2: -These perceptrons sum together the input linear models, and each output a non linear model. The more layers we have in our hidden layer, the more complex non linear models we can find. These models are combined at the output layer to give a final model which should be capable of classifying out input data point.
Detailed explanation-3: -The perceptron outputs 1 for any input point above the hyperplane, and outputs 0 for any input on or below the hyperplane. For this reason, the perceptron is called a linear classifier, i.e., it works well for data that are linearly separable.