  In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%.
