Predicting structured data / (Record no. 72874)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04308nam a2200529 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 6267216 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712204601.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151223s2007 maua ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262255691 |
| -- | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | elelelectronic |
| 245 00 - TITLE STATEMENT | |
| Title | Predicting structured data / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (viii, 348 pages) : |
| 500 ## - GENERAL NOTE | |
| Remark 1 | Collected papers based on talks presented at two Neural Information Processing Systems workshops. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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, 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | BakIr, G�okhan. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267216 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge, Massachusetts : |
| -- | MIT Press, |
| -- | A2007. |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [2007] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/23/2015. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data structures (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Kernel functions. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
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