Abstract:
:Neural networks are often employed as tools in classification tasks. The use of large networks increases the likelihood of the task's being learned, although it may also lead to increased complexity. Pruning is an effective way of reducing the complexity of large networks. We present discriminant components pruning (DCP), a method of pruning matrices of summed contributions between layers of a neural network. Attempting to interpret the underlying functions learned by the network can be aided by pruning the network. Generalization performance should be maintained at its optimal level following pruning. We demonstrate DCP's effectiveness at maintaining generalization performance, applicability to a wider range of problems, and the usefulness of such pruning for network interpretation. Possible enhancements are discussed for the identification of the optimal reduced rank and inclusion of nonlinear neural activation functions in the pruning algorithm.
journal_name
Neural Computjournal_title
Neural computationauthors
Koene RA,Takane Ydoi
10.1162/089976699300016665subject
Has Abstractpub_date
1999-04-01 00:00:00pages
783-802issue
3eissn
0899-7667issn
1530-888Xjournal_volume
11pub_type
杂志文章,评审abstract::The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes and a nonlinear function that r...
journal_title:Neural computation
pub_type: 杂志文章
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更新日期:2010-03-01 00:00:00
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journal_title:Neural computation
pub_type: 信件
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doi:10.1162/0899766053019944
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1997.9.3.503
更新日期:1997-04-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00631
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pub_type: 杂志文章
doi:10.1162/NECO_a_00322
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journal_title:Neural computation
pub_type: 信件
doi:10.1162/NECO_a_00066
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.04-08-745
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016287
更新日期:1999-08-15 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.11-07-651
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01090
更新日期:2018-08-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976606775623342
更新日期:2006-03-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01009
更新日期:2017-12-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1995.7.6.1225
更新日期:1995-11-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766054026693
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976604772744893
更新日期:2004-03-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00615
更新日期:2014-08-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00722
更新日期:2015-05-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602317318947
更新日期:2002-04-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00022
更新日期:2010-10-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603322297331
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pub_type: 杂志文章
doi:10.1162/neco.2007.19.1.139
更新日期:2007-01-01 00:00:00
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journal_title:Neural computation
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doi:10.1162/neco.2010.04-09-989
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602753633411
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321192103
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00882
更新日期:2016-10-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766054323017
更新日期:2005-09-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321891846
更新日期:2003-07-01 00:00:00
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journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00356
更新日期:2012-11-01 00:00:00
abstract::A spiking neuron "computes" by transforming a complex dynamical input into a train of action potentials, or spikes. The computation performed by the neuron can be formulated as dimensional reduction, or feature detection, followed by a nonlinear decision function over the low-dimensional space. Generalizations of the ...
journal_title:Neural computation
pub_type: 杂志文章
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更新日期:2003-08-01 00:00:00