376.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题:  Machine Learning 47(2/3) <4>
发信站: 南京大学小百合站 (Thu Jul 18 00:45:30 2002), 站内信件
篇名： PAC Analogues of Perceptron and Winnow Via Boosting the Margin 
刊名： Machine Learning 
ISSN： 0885-6125 
卷期： 47 卷 2/3 期 出版日期： 200205/06  
页码： 从 133 页到 151 页共 19 页 
作者： Servedio R.   Division of Engineering and Applied Sciences, Harvard
 University, Cambridge, MA, USA, http://www.cs.harvard.edu/~rocco. rocco@
cs.harvard.edu
 
 
文摘： 
We describe a novel family of PAC model algorithms for learning linear threshold
 functions. The new algorithms work by boosting a simple weak learner and
 exhibit sample complexity bounds remarkably similar to those of known online
 algorithms such as Perceptron and Winnow, thus suggesting that these well
-studied online algorithms in some sense correspond to instances of boosting
. We show that the new algorithms can be viewed as natural PAC analogues 
of the online p-norm algorithms which have recently been studied by Grove
, Littlestone, and Schuurmans (1997, Proceedings of the Tenth Annual Conference
 on Computational Learning Theory (pp. 171–183) and Gentile and Littlestone
 (1999, Proceedings of the Twelfth Annual Conference on Computational Learning
 Theory (pp. 1–11). As special cases of the algorithm, by taking p &equals
; 2 and p &equals; ∞ we obtain natural boosting-based PAC analogues of Perce
ptron
 and Winnow respectively. The p &equals; ∞ case of our algorithm can also
 be viewed as a generalization (with an improved sample complexity bound)
 of Jackson and Craven&apos;s PAC-model boosting-based algorithm for learning
 “sparse perceptrons” (Jackson & Craven, 1996, Advances in neural information
 processing systems 8, MIT Press). The analysis of the generalization error
 of the new algorithms relies on techniques from the theory of large margin
 classification.
 


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※ 修改:．GzLi 于 Jul 18 00:47:40 修改本文．[FROM: 211.80.38.29]
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