APPLICATION OF SUPERVISED LEARNING
PERCEPTRON
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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But if the data is not linearly separable, the perceptron can thrash between two or more weight settings.
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improve generalization
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improve accuracy
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improve precision
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Detailed explanation-1: -Adding Noise / Jittering Adding noise or jitter to the inputs during training is also found empirically to improve network generalization. This is because the noise will ‘smear out’ each data point and make it difficult for the network to fit the individual data points precisely, and consequently reduce over-fitting.
Detailed explanation-2: -The averaged perceptron method is an early and simple version of a neural network. In this approach, inputs are classified into several possible outputs based on a linear function, and then combined with a set of weights that are derived from the feature vector-hence the name “perceptron."
Detailed explanation-3: -To reach the best generalization, the dataset should be split into three parts: The training set is used to train a neural net. The error of this dataset is minimized during training. The validation set is used to determine the performance of a neural network on patterns that are not trained during learning.