000 04308nam a2200529 i 4500
001 6267216
003 IEEE
005 20220712204601.0
006 m o d
007 cr |n|||||||||
008 151223s2007 maua ob 001 eng d
020 _z9780262528047
_qprint
020 _a9780262255691
_qebook
020 _z0262255693
_qelelelectronic
035 _a(CaBNVSL)mat06267216
035 _a(IDAMS)0b000064818b419a
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQ325.5
_b.P74 2007eb
245 0 0 _aPredicting structured data /
_cedited by G�okhan Bak�r [and others].
264 1 _aCambridge, Massachusetts :
_bMIT Press,
_c A2007.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2007]
300 _a1 PDF (viii, 348 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
500 _aCollected papers based on talks presented at two Neural Information Processing Systems workshops.
504 _aIncludes bibliographical references (pages 319-340) and index.
506 1 _aRestricted to subscribers or individual electronic text purchasers.
520 _aMachine 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, G�Sokhan Bakir [no dot over i], Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daum� 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 P�rez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Sch�Solkopf, 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 G�Sokhan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in T�ubingen, Germany. Thomas Hofmann is a Director of Engineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of Computer Science at Brown University. Bernhard Sch�Solkopf is Director of the Max Planck Institute for Biological Cybernetics and Professor at the Technical University Berlin. Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania. S. V. N. Vishwanathan is 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.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
550 _aMade available online by EBSCO.
588 _aDescription based on PDF viewed 12/23/2015.
650 0 _aData structures (Computer science)
_98188
650 0 _aKernel functions.
_921554
650 0 _aComputer algorithms.
_94534
650 0 _aMachine learning.
_91831
655 0 _aElectronic books.
_93294
700 1 _aBakIr, G�okhan.
_921555
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_921556
710 2 _aMIT Press,
_epublisher.
_921557
710 2 _aNeural Information Processing Systems Foundation.
_921558
776 0 8 _iPrint version
_z9780262528047
830 0 _aNeural information processing series.
_921559
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267216
942 _cEBK
999 _c72874
_d72874