Large-scale SVM training suffers from computational complexity and memory requirements. Local-to-global SVMs (LGSVMs) address this by decomposing the domain into overlapping patches using semi-metrics (Euclidean or cosine), training local SVM classifiers independently on each subdomain, and combining predictions via partition of unity weights. Theoretical analysis shows LGSVMs preserve local classification accuracy while reducing validation time significantly, and the partition of unity framework ensures smooth global decision boundaries. Numerical experiments on high-dimensional datasets demonstrate competitive accuracy with substantial computational speedup compared to global SVMs.