Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Margin Distribution Controlled Boosting   Schapire's margin theory provides a theoretical explanation to the success of
boosting-type methods and manifests that a good margin distribution (MD) of
training samples is essential for generalization. However the statement that a
MD is good is vague, consequently, many recently developed algorithms try to
generate a MD in their goodness senses for boosting generalization. Unlike
their indirect control over MD, in this paper, we propose an alternative
boosting algorithm termed Margin distribution Controlled Boosting (MCBoost)
which directly controls the MD by introducing and optimizing a key adjustable
margin parameter. MCBoost's optimization implementation adopts the column
generation technique to ensure fast convergence and small number of weak
classifiers involved in the final MCBooster. We empirically demonstrate: 1)
AdaBoost is actually also a MD controlled algorithm and its iteration number
acts as a parameter controlling the distribution and 2) the generalization
performance of MCBoost evaluated on UCI benchmark datasets is validated better
than those of AdaBoost, L2Boost, LPBoost, AdaBoost-CG and MDBoost.

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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn