Discriminative learning framework is one of the very successful fields of machine learn-ing. The methods of this paradigm, such as Boosting and Support Vector Machines, have significantly advanced the state-of-the-art for classification by improving the ac-curacy and by increasing the applicability of machine learning methods. One of the key benefits of these methods is their ability to learn efficiently in high dimensional fea-ture spaces, either by the use of implicit data representations via kernels or by explicit feature induction. However, traditionally these methods do not exploit dependencies between class labels where more than one label is predicted. Many real-world classifica-tion problems involve sequential, temporal or structural dependencies between multiple labels. The goal of this research is to generalize discriminative learning methods for such scenarios. In particular, we focus on label sequence learning. Label sequence learning is the problem of inferring a state sequence from an ob-servation sequence, where the state sequence may encode a labeling, an annotation or a segmentation of the sequence. Prominent examples include part-of-speech tagging, named entity classification, information extraction, continuous speech recognition, and secondary protein structure prediction. In this thesis, we present three novel discriminative methods that are generalizations of AdaBoost and multiclass Support Vector Machines (SVM) and a Gaussian Process formulation for label sequence learning. These techniques combine the efficiency of dynamic programming methods with the advantages of the state-of-the-art learning methods. We present theoretical analysis and experimental evaluations on pitch accent prediction, named entity recognition and part-of-speech tagging which demonstrate the advantages over classical approaches like Hidden Markov Models as well as the state-of-the-art methods like Conditional Random Fields. i
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