A METHOD OF LEARNING FROM EIGENVALUE TRANSITION OF EXAMPLES BASED ON INFORMATION GAIN

A method of learning eigenvalues and their varieties is given. It improves the method ID3 which learns from static eigenvalues of examples. The varieties and dynamic correlation of eigenvalues are ignored in the method ID3. In the given method, the change of data can be learned because the training data is the initial and end eigenvalue of the interval. All eigenvalue’s varieties and correlation can be understood and remembered. The method can be applied as classifier when the multi-parameters are dynamically associated.