Few-shot image classification aims to learn an embedding model on the base datasets and design a base learner to recognize novel categories. The few-shot image classification framework is a two-phase process. First, the pre-train phase utilizes the base data to train a CNN-based feature extractor. Next, in the meta-test phase, the frozen feature extractor is applied to novel data with categories different from the base data. A base learner is then designed for recognition. Several simple base learners, including nearest neighbor, support vector machine, and logistic regression classifiers, have been recently introduced for few-shot learning tasks. However, these base learners are separately designed to consider specific representations (e.g., the class center) or shared representations (e.g., the boundaries). This paper mainly focuses on exploring the representation-residual base learners, which aim to represent a query sample with the support set and predict the query sample's label based on the minimal residual error. We first introduce two representation-residual base learners: a specific representation base learner and a shared representation base learner. Then, we propose a novel hybrid representation base learner that combines both base learners to generate competitive representation. Additionally, we extend our approach by incorporating a self-training framework to utilize the query data fully. We evaluate our proposed method on several benchmark few-shot image classification datasets, such as miniImageNet, tieredImageNet, CIFAR-FS, FC100, and CUB datasets. The experimental results indicate that our proposed approach shows a significant performance improvement.