1. A rule learning method for making a computer perform rule learning processing in machine learning, the method comprising: firstly calculating an evaluation value to select a feature to be calculated for a confidence value for respective features registered in a training example data storage unit that stores a plurality of combinations of a training example and a weight of the training example, each training example including a feature-set with one or more features and a label indicating that the example is either true or false, by using data of the training examples and the weights of the training examples, and storing the calculated evaluation value in correspondence with the feature in an evaluation value storage unit; selecting a given number of features in descending order of the evaluation values stored in the evaluation value storage unit; secondly calculating of the confidence value for one of the given number of selected features, by using the data and the weights of the training examples registered in the training example data storage unit, and storing a combination of the confidence value and the one of the selected features in a rule data storage unit; updating the weights stored in the training example data storage unit, by using the data and the weights of the training examples and the confidence value corresponding to the one feature; firstly repeating the updating for a remaining feature of the given number of features; and secondly repeating, for a given number of times, the firstly calculating, the selecting, the secondly calculating, the updating, and the firstly repeating.