Few-shot learning is a challenging task in computer vision, where the goal is to classify objects or entities based on very limited training examples. In this paper, we propose a novel approach that combines deep Brownian distance covariance (DeepBDC), few-shot classification with cosine similarity (FS-CS), and support vector machines (SVM) to improve few-shot learning accuracy. We present extensive empirical results on the CUB-200 dataset, demonstrating the effectiveness of our approach. In our experiments, we investigate the impact of different similarity functions, including Euclidean distance, cosine similarity, and inner multiplication, on few-shot classification accuracy. Our results show that the SVM-based similarity function outperforms other methods, achieving the highest accuracy in both 1-shot and 5-shot settings. These findings suggest that SVM-based similarity functions can significantly enhance few-shot learning. Furthermore, we compare our approach to existing models and highlight its advantages in terms of accuracy and efficiency. Our research contributes to the field of few-shot learning by introducing a novel combination of techniques and innovative methodologies, which can pave the way for further advancements in this area.