Predicting Structured Data

Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning in which the prediction must satisfy the additional constraints found in structured data poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation document markup computational biology and information extraction among others providing a timely overview of an exciting field.Contributors Yasemin Altun Gökhan Bakir Olivier Bousquet Sumit Chopra Corinna Cortes Hal Daumé III Ofer Dekel Zoubin Ghahramani Raia Hadsell Thomas Hofmann Fu Jie Huang Yann LeCun Tobias Mann Daniel Marcu David McAllester Mehryar Mohri William Stafford Noble Fernando Pérez-Cruz Massimiliano Pontil Marc'Aurelio Ranzato Juho Rousu Craig Saunders Bernhard Schölkopf Matthias W. Seeger Shai Shalev-Shwartz John Shawe-Taylor Yoram Singer Alexander J. Smola Sandor Szedmak Ben Taskar Ioannis Tsochantaridis S.V.N Vishwanathan Jason Weston.