Summary: Online MKL for Structured Prediction
Andr´e F. T. Martins
Noah A. Smith
Eric P. Xing

School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
Pedro M. Q. Aguiar

Instituto de Sistemas e Rob´otica,
Instituto Superior T´ecnico, Lisboa, Portugal
M´ario A. T. Figueiredo

Instituto de Telecomunicac¸~oes,
Instituto Superior T´ecnico, Lisboa, Portugal
1 Introduction
Structured prediction (SP) problems are characterized by strong interdependence among the output
variables, usually with sequential, graphical, or combinatorial structure [17, 7]. Obtaining good
predictors often requires a large effort in feature/kernel design and tuning (usually done via cross-
validation). Because discriminative training of structured predictors can be quite slow, specially in
large scale settings, it is appealing to learn the kernel function simultaneously.

Source: Aguiar, Pedro M. Q. - Institute for Systems and Robotics (Lisbon)

Collections: Engineering