In this paper, we develop a new method for feature selection and category learning. We first introduce two observations from our experiments: (1) It is easier to distinguish two concepts than to learn an isolated concept. (2) To distinguish different concept pairs we can find different selections of optimal features. These two observations may partly explain the success of human vision learning, especially why an infant can simultaneously capture distinguished visual features when learning new concepts. Based on these two observations, we developed a new learning-by-focusing method which first constructs focalized concept discriminators for pairs of concepts, and then builds nonlinear classifiers using the discrimination scores. We build datasets for four concept structure: vehicle, human affliction, sports, and animals, and experiments on all the four datasets verify the success of our new approach. © 2013 IEEE.