Unsupervised classification of noisy chromosomes

摘要: Motivation: Almost all methods of chromosome recognition assume supervised training; i.e. we are given correctly classified chromosomes to start the training phase. Noise, if any, is confined only in representation and not classification chromosomes. During phase, problem simply calculate string edit distance unknowns representatives chosen from phase classify accordingly. Results: In this paper, a general method tackle difficult unsupervised induction described. The success demonstrated by showing how inductive agent learns weights dynamic manner that allows it distinguish between noisy median telocentric without knowing their proper labels. process learning characterized as finding right function, function can nicely separate classes.