Support vector machines (SVMs) are widely used machine learning models (e.g.,
in remote sensing), with formulations for both classification and regression
tasks. In the last years, with the advent of working quantum annealers, hybrid
SVM models characterised by quantum training and classical execution have been
introduced. These models have demonstrated comparable performance to their
classical counterparts. However, they are limited in the training set size due
to the restricted connectivity of the current quantum annealers. Hence, to take
advantage of large datasets (like those related to Earth observation), a
strategy is required. In the classical domain, local SVMs, namely, SVMs trained
on the data samples selected by a k-nearest neighbors model, have already
proven successful. Here, the local application of quantum-trained SVM models is
proposed and empirically assessed. In particular, this approach allows
overcoming the constraints on the training set size of the quantum-trained
models while enhancing their performance. In practice, the FaLK-SVM method,
designed for efficient local SVMs, has been combined with quantum-trained SVM
models for binary and multiclass classification. In addition, for comparison,
FaLK-SVM has been interfaced for the first time with a classical single-step
multiclass SVM model (CS SVM). Concerning the empirical evaluation, D-Wave's
quantum annealers and real-world datasets taken from the remote sensing domain
have been employed. The results have shown the effectiveness and scalability of
the proposed approach, but also its practical applicability in a real-world
large-scale scenario.

SVM 与量子退火器相结合的 FaLK-SVM 方法在处理大规模实际数据集时展现出了良好的性能和可扩展性。