Appropriate Problems for Neural Network Learning



Appropriate Problems for Neural Network Learning

  • ANN learning is well-suited to problems in which the training data corresponds to noisy, complex sensor data, such as inputs from cameras and microphones. 

  • The BACKPROPAGATION algorithm is the most commonly used ANN learning technique.

  • ANN learning is well-suited to problems in which the training data corresponds to noisy, complex sensor data, such as inputs from cameras and microphones. 

  • The BACKPROPAGATION algorithm is the most commonly used ANN learning technique.

It is appropriate for problems with the following characteristics:

  • Instances are represented by many attribute-value pairs.

  • The target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes.

  • The training examples may contain errors.

  • Long training times are acceptable.

  • Fast evaluation of the learned target function may be required.

  • The ability of humans to understand the learned target function is not important.

It is appropriate for problems with the following characteristics:

  • Instances are represented by many attribute-value pairs.

  • The target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes.

  • The training examples may contain errors.

  • Long training times are acceptable.

  • Fast evaluation of the learned target function may be required.

  • The ability of humans to understand the learned target function is not important.