1 research outputs found
Structured Prediction Theory Based on Factor Graph Complexity
We present a general theoretical analysis of structured prediction with a
series of new results. We give new data-dependent margin guarantees for
structured prediction for a very wide family of loss functions and a general
family of hypotheses, with an arbitrary factor graph decomposition. These are
the tightest margin bounds known for both standard multi-class and general
structured prediction problems. Our guarantees are expressed in terms of a
data-dependent complexity measure, factor graph complexity, which we show can
be estimated from data and bounded in terms of familiar quantities. We further
extend our theory by leveraging the principle of Voted Risk Minimization (VRM)
and show that learning is possible even with complex factor graphs. We present
new learning bounds for this advanced setting, which we use to design two new
algorithms, Voted Conditional Random Field (VCRF) and Voted Structured Boosting
(StructBoost). These algorithms can make use of complex features and factor
graphs and yet benefit from favorable learning guarantees. We also report the
results of experiments with VCRF on several datasets to validate our theory