The problem of learning a mapping between input and structured, interdependent output variables covers sequential, spatial, and relational learning as well as predicting recursive structures. Joint feature representations of the input and output variables have paved the way to leveraging discriminative learners such as SVMs to this class of problems. We address the problem of semi-supervised learning in joint input output spaces. The cotraining approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised support vector learning algorithm for joint input output spaces and arbitrary loss functions. Experiments investigate the benefit of semisupervised structured models in terms of accuracy and F1 score.
2. Brefeld and Scheffer REF proposed to solve the problem of semi-supervised structured output prediction by learning in the space of input-output space, and using co-training method.
Semi-supervised learning for structured output variables
ICML '06
Computer Science