As a support vector machine(SVM) is determined only by support vectors(SVs),which are a part of edge samples,its generalization ability may be decreased if the noise is too much or outlier samples are too many,especially the samples from different classes are intermixed excessively.

A general uncertainty relation between the learning ability and the generalization ability suited to overfitting of BP neural network was revealed in the modeling of BP neural network by analogy of general uncertainty relation in information transmitting processes.

Kernel parameter selection directly affects the model s generalization ability.

In the machine learning problems,the consistence and generalization performance are the main research points.

A new constructive principle, which depends on the distribution of examples, for measuring the generalization performance is proposed based on the analysis of the generalization performance of support vector machines.

Simulation results for both artificial and real data show the generalization performance of our method is a good approximation of SVMs and the computation complexity is largely reduced by our method.

This paper studies the generalization capability of feedforward neural networks (FNN).

Research on learning ability of liberal arts-oriented and science-oriented students majoring in clinical medicine;

Attempts of training the learning ability of medical students in genetics teaching process;

Effect of prenatal lead exposure on offspring s learning ability and prevention;