Predicting Structured Data

Machine learning develops intelligent computer systems that are able to generalizefrom previously seen examples. A new domain of machine learning, in which the prediction mustsatisfy the additional constraints found in structured data, poses one of machine learning'sgreatest 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 inthis novel field. The contributors discuss applications as diverse as machine translation, documentmarkup, computational biology, and information extraction, among others, providing a timely overviewof an exciting field. Contributors Yasemin Altun, Gokhan Bakir [no dot over i], Olivier Bousquet,Sumit Chopra, Corinna Cortes, Hal Daume III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, ThomasHofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri,William Stafford Noble, Fernando Perez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu,Craig Saunders, Bernhard Scholkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor,Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.NVishwanathan, Jason Weston Gokhan Bakir [no dot over i] is Research Scientist at the Max PlanckInstitute for Biological Cybernetics in Tubingen, Germany. Thomas Hofmann is a Director ofEngineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of ComputerScience at Brown University. Bernhard Scholkopf is Director of the Max Planck Institute forBiological Cybernetics and Professor at the Technical University Berlin. Alexander J. Smola isSenior Principal Researcher and Machine Learning Program Leader at National ICT Australia/AustralianNational University, Canberra. Ben Taskar is Assistant Professor in the Computer and InformationScience Department at the University of Pennsylvania. S. V. N. Vishwanathan is Senior Researcher inthe Statistical Machine Learning Program, National ICT Australia with an adjunct appointment at theResearch School for Information Sciences and Engineering, Australian National University.

S. V. N. Vishwanathan is an Assistant Professor of Statistics and Computer Science at Purdue University and Senior Researcher in the Statistical Machine Learning Program, National ICT Australia with an adjunct appointment at the Research School for Information Sciences and Engineering, Australian National University.