Composite hypothesis in Machine Learning

As the order of the integration method is increased, the order of the derivative in the error term associated with the method also increases. For any method to produce meaningful results, these higher-order derivatives must remain continuous in the interval of interest. Also, Newton-cotes type methods of higher order sometimes produce diverging results. An alternative to obtain accurate results, while using lower-order methods is the use of composite integration methods. We subdivide the given interval [a,b] or [-1,1] into a number of subintervals and evaluate the integral in each subinterval by a particular method. This is known as composite or multisegnent hypothesis