3.8 Basis Functions in Neural Networks

Basis Functions in Neural Networks: -

  • Basis functions are mathematical functions used to transform input data into a new form so that machine learning models can learn patterns more easily.
  • Sometimes the relationship between input and output is not simple or linear. They help convert complex relationships into simpler forms and improve prediction accuracy.
  • Examples include polynomial functions and activation functions such as ReLU and Sigmoid used in neural networks.
  • In neural networks, activation functions act like basis functions. They transform the input values so the network can learn complex relationships.

Common examples include:

  • Sigmoid function

  • ReLU function

  • Tanh function

These functions convert the input into non-linear outputs, helping the network learn better.

Example

Suppose we want to predict student performance based on study hours.

Input:       Study hours = 2, 4, 6, 8

But the relationship between study hours and marks may not be perfectly straight. A basis function can transform the data.

Example transformation:

  • Original input: xx

  • Basis function: x2x^2

If study hours = 4

New value = 42=164^2 = 16

This transformation helps the model capture more complex patterns.





























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