1. A machine learning device comprising: a processor and a memory storing a program which is executable by the processor to perform operations comprising: acquiring n learning contents (n is a natural number larger than or equal to 2) with a label to be used for categorization; acquiring a feature vector from each of the n learning contents; converting the feature vectors for the n learning contents to similarity feature vectors based on similarity degrees between the learning contents; learning a classification condition for categorizing the n learning contents based on the similarity feature vectors and the label assigned to each of the n learning contents; and categorizing a testing content to which the label is not assigned, in accordance with the learned classification condition; wherein the labels are for categorizing the n learning contents into a plurality of categories; wherein the processor learns a function for linear separation as the classification condition in order to classify n points in a vector space determined by the similarity feature vectors of the n learning contents into the plurality of categories, based on the labels assigned to the n learning contents; and wherein the processor classifies the testing content into one of the plurality of categories in accordance with the function.