Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR&SPR 2012, Hiroshima, Japan, November 7-9, 2012. Proceedings
Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR&SPR 2012, Hiroshima, Japan, November 7-9, 2012. Proceedings
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结构、句法和统计模式识别 - IAPR 国际联合研讨会、SSPR
DOI：
10.1007/978-3-642-34166-3_77
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Windeatt T
中科院分区：
文献类型：
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作者：
Windeatt T
如果对集合中的每个分类器进行二进制判决，则训练模式可以被表示为二进制向量。对于两类监督学习问题，这将导致部分指定的布尔函数，该布尔函数可以根据谱系数进行分析。在本文中，证明了以系数加权的投票能够使快速集成分类器获得接近贝叶斯率的性能。实验结果表明，使用带有隐含节点的Lvenberg-MarQuardt算法对MLP进行一个周期的训练，就可以获得有效的分类器性能。
If a binary decision is taken for each classifier in an ensemble, training patterns may be represented as binary vectors. For a two-class supervised learning problem this leads to a partially specified Boolean function that may be analysed in terms of spectral coefficients. In this paper it is shown that a vote which is weighted by the coefficients enables a fast ensemble classifier that achieves performance close to Bayes rate. Experimental evidence shows that effective classifier performance may be achieved with one epoch of training of an MLP using Levenberg-Marquardt with 64 hidden nodes.