Mixed-signal machine-learning classification has recently been demonstrated as an efficient alternative for classification with power expensive digital circuits. In this paper, a high-COnfidence high-REsolution (CORE) mixed-signal classifier is proposed for classifying high-dimensional input data into multi-class output space with less power and area than state-of-the-art classifiers. A high-resolution multiplication is facilitated within a single-MOSFET by feeding the features and feature weights into, respectively, the body and gate inputs. High-resolution classifier that considers the confidence of the individual predictors is designed at 45 nm technology node and operates at 100 MHz in subthreshold region. To evaluate the performance of the classifier, a reduced MNIST dataset is generated by downsampling the MNIST digit images from 28 $\times$ 28 features to 9 $\times$ 9 features. The system is simulated across a wide range of PVT variations, exhibiting nominal accuracy of 90%, energy consumption of 6.2 pJ per classification (over 45 times lower than state-of-the-art classifiers), area of 2,179 $μ$$m^{2}$ (over 7.3 times lower than state-of-the-art classifiers), and a stable response under PVT variations.