This paper explores the use of cutting-plane methods to train structural support vector machines (SVMs), which are effective for building complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. Current training algorithms for SVMs are computationally expensive or intractable on large datasets. The authors propose a cutting-plane method that has linear time complexity in the number of training examples, making it significantly faster than conventional methods. They demonstrate that the size of the cutting-plane models and the number of iterations are independent of the number of training examples, and can be bounded by \(O(\frac{C}{\varepsilon})\), where \(C\) is the regularization constant and \(\varepsilon\) is the desired precision. Empirical evaluations on various structured prediction problems show that the proposed method is substantially faster than conventional decomposition methods and cutting-plane methods, often by several orders of magnitude for large datasets. The method is applicable to all structural SVM problems where an efficient separation oracle can be computed, making it widely applicable.This paper explores the use of cutting-plane methods to train structural support vector machines (SVMs), which are effective for building complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. Current training algorithms for SVMs are computationally expensive or intractable on large datasets. The authors propose a cutting-plane method that has linear time complexity in the number of training examples, making it significantly faster than conventional methods. They demonstrate that the size of the cutting-plane models and the number of iterations are independent of the number of training examples, and can be bounded by \(O(\frac{C}{\varepsilon})\), where \(C\) is the regularization constant and \(\varepsilon\) is the desired precision. Empirical evaluations on various structured prediction problems show that the proposed method is substantially faster than conventional decomposition methods and cutting-plane methods, often by several orders of magnitude for large datasets. The method is applicable to all structural SVM problems where an efficient separation oracle can be computed, making it widely applicable.