Major　depressive　disorder　(MDD)　is　a　leading　world-wide　psychiatric　disorder　with　high　recurrence　rate,　therefore,　it　is　desirable　to　identify　current　MDD　(cMDD)　and　remitted　MDD　(rMDD)　for　their　appropriate　therapeutic　interventions.　In　the　study,　19　cMDD,　19　rMDD　and　19　well-matched　healthy　controls　(HC)　were　enrolled　and　scanned　with　the　resting-state　functional　magnetic　resonance　imaging　(rs-fMRI).　The　Hurst　exponent　(HE)　of　rs-fMRI　in　AAL-90　and　AAL-1024　atlases　were　calculated　and　compared　between　groups.　Then,　a　radial　basis　function　(RBF)　based　support　vector　machine　was　proposed　to　identify　every　pair　of　the　cMDD,　rMDD　and　HC　groups　using　the　abnormal　HE　features,　and　a　leave-one-out　cross-validation　was　used　to　evaluate　the　classification　performance.　Applying　the　proposed　method　with　AAL-1024　and　AAL-90　atlas　respectively,　87%　and　84%　subjects　were　correctly　identified　between　cMDD　and　HC,　84%　and　71%　between　rMDD　and　HC,　and　89%　and　74%　between　cMDD　and　rMDD.　Our　results　indicated　that　the　HE　was　an　effective　feature　to　distinguish　cMDD　and　rMDD　from　HC,　and　the　recognition　performances　with　AAL-1024　parcellation　were　better　than　that　with　the　conventional　AAL-90　parcellation.