Partially　supervised　learning　extends　both　supervised　and　unsupervised　learning,　by　considering　situations　in　which　only　partial　information　about　the　response　variable　is　available.　In　this　paper,　we　consider　partially　supervised　classification　and　we　assume　the　learning　instances　to　be　labeled　by　Dempster-Shafer　mass　functions,　called　soft　labels.　Linear　discriminant　analysis　and　logistic　regression　are　considered　as　special　cases　of　generative　and　discriminative　parametric　models.　We　show　that　the　evidential　EM　algorithm　can　be　particularized　to　fit　the　parameters　in　each　of　these　models.　We　describe　experimental　results　with　simulated　data　sets　as　well　as　with　two　real　applications:　K-complex　detection　in　sleep　EEGs　signals　and　facial　expression　recognition.　These　results　confirm　the　interest　of　using　soft　labels　for　classification　as　compared　to　potentially　erroneous　crisp　labels,　when　the　true　class　membership　is　partially　unknown　or　ill-defined.