Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Summary Of The Kernel Matrix, And How To Learn It Effectively Using
  Semidefinite Programming   Kernel-based learning algorithms are widely used in machine learning for
problems that make use of the similarity between object pairs. Such algorithms
first embed all data points into an alternative space, where the inner product
between object pairs specifies their distance in the embedding space. Applying
kernel methods to partially labeled datasets is a classical challenge in this
regard, requiring that the distances between unlabeled pairs must somehow be
learnt using the labeled data. In this independent study, I will summarize the
work of G. Lanckriet et al.'s work on "Learning the Kernel Matrix with
Semidefinite Programming" used in support vector machines (SVM) algorithms for
the transduction problem. Throughout the report, I have provide alternative
explanations / derivations / analysis related to this work which is designed to
ease the understanding of the original article.

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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga