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Machine learning methods can be employed to discover the relationship between inputs and their desired outputs from a large collection of data points. The outputs of many real-world problems are naturally formed as structured objects in which elements are interdependent in terms of the given structure. Although the conventional methods may be directly applied to solve these problems by treating the elements in each output object independently, they tend to yield suboptimal performance because of the ignorance of interdependency information in the structured outputs. During the last few years, structured prediction provides a natural way to directly model the relationship between inputs and structured outputs. Structure prediction model consists of three components: feature engineering, learning the optimal hypothesis from data by minimizing specific loss, and prediction. In the thesis, the main focus is on the first two components which are interweaved in the following three pieces of my works, where the generalized linear model is used in the prediction. The first work of this thesis deals with the feature engineering problem which is critical in the structured prediction modeling. The success of structure prediction models is attributed to the fact that their discriminative models are able to account for overlapping features on the whole input observations. These features are usually generated from data by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate the difficulty of template selection in feature engineering phrase, a novel multiple template learning paradigm has been proposed to learn a structured prediction model and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution of degraded performance. This paradigm has been further extended for structured prediction using generalized p-block norm regularization.

Structured prediction for feature selection and performance evaluation