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  <id>923266</id>
  <name><![CDATA[Michael I. Jordan]]></name>
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  <id type="integer">5680959</id>
  <isbn>026256145X</isbn>
  <isbn13>9780262561457</isbn13>
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  <title>
    <![CDATA[Advances in Neural Information Processing Systems: Proceedings of the First 12 Conferences]]>
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  <link>http://www.goodreads.com/book/show/5680959.Advances_in_Neural_Information_Processing_Systems_Proceedings_of_the_First_12_Conferences</link>
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  <description>
    <![CDATA[The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This CD-ROM contains the entire proceedings of the twelve Neural Information Processing Systems conferences from 1988 to 1999. The files are available in the DjVu image format developed by Yann LeCun and his group at AT&amp;T Labs. The CD-ROM includes free browsers for all major platforms.]]>
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    <author>
    <id>923266</id>
        <name><![CDATA[Michael I. Jordan]]></name>
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    <link><![CDATA[http://www.goodreads.com/author/show/923266.Michael_I_Jordan]]></link>
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    <author>
    <id>2541804</id>
        <name><![CDATA[Yann LeCun]]></name>
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    <link><![CDATA[http://www.goodreads.com/author/show/2541804.Yann_LeCun]]></link>
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    <author>
    <id>1039258</id>
        <name><![CDATA[Sara A. Solla]]></name>
    <image_url><![CDATA[http://www.goodreads.com/images/nophoto/nophoto-U-200x266.jpg]]></image_url>
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    <link><![CDATA[http://www.goodreads.com/author/show/1039258.Sara_A_Solla]]></link>
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  </author>
  </authors>  <published>2001</published>
</book>

        <book>
  <id type="integer">2029691</id>
  <isbn>0262600323</isbn>
  <isbn13>9780262600323</isbn13>
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  <title>
    <![CDATA[Learning in Graphical Models]]>
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  <image_url>http://www.goodreads.com/images/nocover-111x148.jpg</image_url>
  <small_image_url>http://www.goodreads.com/images/nocover-60x80.jpg</small_image_url>
  <link>http://www.goodreads.com/book/show/2029691.Learning_in_Graphical_Models</link>
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    <![CDATA[Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.<br/> <br/> 	This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.]]>
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    <author>
    <id>923266</id>
        <name><![CDATA[Michael I. Jordan]]></name>
    <image_url><![CDATA[http://www.goodreads.com/images/nophoto/nophoto-U-200x266.jpg]]></image_url>
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    <link><![CDATA[http://www.goodreads.com/author/show/923266.Michael_I_Jordan]]></link>
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  </authors>  <published>1998</published>
</book>

        <book>
  <id type="integer">6830499</id>
  <isbn>0262100657</isbn>
  <isbn13>9780262100656</isbn13>
  <text_reviews_count type="integer">0</text_reviews_count>
  <title>
    <![CDATA[Advances in Neural Information Processing Systems 9]]>
  </title>
  <image_url>http://www.goodreads.com/images/nocover-111x148.jpg</image_url>
  <small_image_url>http://www.goodreads.com/images/nocover-60x80.jpg</small_image_url>
  <link>http://www.goodreads.com/book/show/6830499-advances-in-neural-information-processing-systems-9</link>
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  <description>
    <![CDATA[The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes neural networks and genetic algorithms, cognitive science, neuroscience and biology, computer science, AI, applied mathematics, physics, and many branches of engineering. Only about 30% of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. All of the papers presented appear in these proceedings.]]>
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<authors>
    <author>
    <id>3064825</id>
        <name><![CDATA[Michael C. Mozer]]></name>
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    <author>
    <id>923266</id>
        <name><![CDATA[Michael I. Jordan]]></name>
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    <small_image_url><![CDATA[http://www.goodreads.com/images/nophoto/nophoto-U-50x66.jpg]]></small_image_url>
    <link><![CDATA[http://www.goodreads.com/author/show/923266.Michael_I_Jordan]]></link>
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    <author>
    <id>2919620</id>
        <name><![CDATA[Thomas Petsche]]></name>
    <image_url><![CDATA[http://www.goodreads.com/images/nophoto/nophoto-U-200x266.jpg]]></image_url>
    <small_image_url><![CDATA[http://www.goodreads.com/images/nophoto/nophoto-U-50x66.jpg]]></small_image_url>
    <link><![CDATA[http://www.goodreads.com/author/show/2919620.Thomas_Petsche]]></link>
    <average_rating>0.0</average_rating>
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  </author>
  </authors>  <published>1997</published>
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