In multi-class classification, the cost for misclassification may vary over each class. This is a situation in structured learning, where the focus is how to leverage dependency among different classes to enhance the performance of classification that ignores such dependency structure. Examples include hierarchical classification, sequence alignment, and natural language processing, among others. This paper develops a framework for multi-class margin classification with un-equal (equal) cost. Within the framework, structured learning is formulated, where the dependency is taken into account through the cost of misclassification. This framework is implemented for support vector machines. An application to hierarchical classification is discussed. In addition, some simulations are performed, indicating
that the proposed methodology achieves the desired objective.