The properties of learning machines with the kernel methods, such as support vector machines or kernel perceptrons, are examined for simple cases. We first elucidated that the number of effective examples depends on the condition of the true parameter. Next, the prediction errors of some algorithms were analyzed. The derived errors do not depend on the dimension of the feature space.
