This paper presents an embedded learning algorithm, a one-layer VMM + WTA classifier, on a Large-Scale Field Programmable Analog Array (FPAA), The technique enables opportunities for embedded, ultra-low power machine learning, techniques typically considered for large servers. A VMM + WTA single, one-layer network is a universal approximator. An on-chip learning algorithm was developed to train this physical classifier. A clustering step determines the initial weight set for ideal target and background values. Null symbols are important for the algorithm and are set from midpoints of the target values. Experimental measurements are shown for this learning classifier implemented on an SoC FPAA device.