[求助]哪位英语能力强的帮我翻译下(页 1) - 计算机英语

宇风发表于 2007-2-19 20:26

[求助]哪位英语能力强的帮我翻译下

一篇关于数据挖掘决策树有关知识的文章 小弟菜鸟 哪位大吓帮忙翻译下 The purpose of the decision tree classiﬁer is to classify instances based on values of ordinary attributes and class label attribute. Traditionally, the data set is single-valued and single-labeled. In this data set, each record has many single- valued attributes and a given single-labeled attribute (i.e. class label attribute), and the class labels that can have two or more than two types are exclusive to each other or one another. Prior art decision tree classiﬁers, such as ID3 (<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Quinlan, 1979, 1986</A>), Distance-based method (<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Mantaras,</A> <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >1991</A>),IC(<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Agrawal, Ghosh, Imielinski, Iyer, & Swami,</A> <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >1992</A>), C4.5 (<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Quinlan, 1993</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >),</A> Fuzzy ID3 <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >(</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Umano et al., 1994</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >),</A> CART (<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Steinberg & Colla, 1995</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >),</A> SLIQ <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >(</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Mehta, Agrawal, &</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Rissanen, 1996</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >),</A> SPRINT (<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Shafer, Agrawal, & Mehta, 1996</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >),</A> Rainforest (<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Gehrke, Ramakrishnan, & Ganti, 1998</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >) </A>and PUBLIC (<a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Rastogi & Shim, 1998</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >), </A>all focus on this single- valued and single-labeled data set.<o:p></o:p>
 However, there is multi-valued and multi-labeled data in the real world as shown in <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#2" target="_blank" >Table 1</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#2" target="_blank" >. </A>Multi-valued data means that a record can have multiple values for an ordinary <o:p></o:p>
attribute. Multi-labeled data means that a record can belong to multiple class labels, and the class labels are not exclusive to each other or one another. Readers might have difﬁculties to distinguish multi-labeled data from two-classed or multi- classed data mentioned in some related works. To clarify<o:p></o:p>
this confusion, we discuss the exclusiveness among classes, number of class and representation of the class label attribute in the related works as follows:<o:p></o:p>
1. Exclusiveness: Each data can only belong to a single class. Classes are exclusive to one another. ID3,<o:p></o:p>
Distance-based Method, IC, C4.5, Fuzzy ID3, CART, SLIQ, SPRINT, Rainforest and PUBLIC are such examples.<o:p></o:p>
2. Number of class: Data with classes classiﬁed into two types in the class label attribute is called two-classed data. ID3 and C4.5 are such examples. Data with classes classiﬁed into more than two types in the class label attribute is called multi-classed data. IC, CART and Fuzzy ID3 are such examples.<o:p></o:p>
3. Label representation: Data with a single value for the class label attribute is called single-labeled data. ID3, Distance-based Method, IC, C4.5, Fuzzy ID3, CART, SLIQ, SPRINT, Rainforest and PUBLIC are such examples.<o:p></o:p>
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According to the discussion above, a multi-valued and multi-labeled data as we deﬁned here can beregarded as a non-exclusive, multi-classed and multi-labeled data.<o:p></o:p>
In our previous work <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >(</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Chen, Hsu, & Chou, 2003</A>), we have explained why the traditional classiﬁers are not capable of handling this multi-valued and multi-labeled data. To solve this multi-valued and multi-labeled classiﬁ-cation problem, we have designed a decision tree classiﬁer named MMC <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >(</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Chen et al., 2003</A>) before. MMC differs from the traditional ones in some major functions including growing a decision tree, assigning labels to represent a leaf and making a prediction for a new data. In the process of growing a tree, MMC proposes a new measure named weighted similarity for selecting multi-valued attribute to partition a node into child nodes to approach perfect grouping. To assign labels, MMC picks the ones with numbers large enough to represent a leaf. To make a prediction for a new data, MMC traverses the tree as usual, and as the traversing reaches several leaf nodes for the record with multi-valued attribute, MMC would union all the labels of the leaf nodes as the prediction result. Experimental results show that MMC can get an average predicting accuracy of 62.56%.<o:p></o:p>
Having a decision classiﬁer developed for the multi-valued and multi-labeled data, this research steps further to<o:p></o:p>
improve the classiﬁer’s accuracy. Considering the following over-ﬁtting problems <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >(</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Han & Kamber, 2001; Russell &</A> <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Norving, 1995</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >) </A>of MMC, improvement on its predictingaccuracy seems possible. First, MMC neglects to avoid the situation when the data set is too small. Therefore, it may choose some attributes irrelevant to the class labels. Second, MMC appears to prefer the attribute which splits into child nodes with larger similarity among multiple<o:p></o:p>
labels. Therefore, MMC exists inductive bias <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >(</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Gordon &</A> <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >Desjardins, 1995</A><a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#13" target="_blank" >).</A><o:p></o:p>
Trying to minimize the over-ﬁtting problems above, this paper proposes solutions as: (1) Set a constraint of size for the data set in each node to avoid the data set being too small. (2) Consider not only the average similarity of labels of each child node but also the average appropriateness of labels of<o:p></o:p>
each child node to decrease the bias problem of MMC.Based on the propositions above, we have designed a new decision tree classiﬁer to improve the accuracy of MMC.The decision tree classiﬁer, named MMDT (multi-valued and multi-labeled decision tree), can construct a multi-<o:p></o:p>
valued and multi-labeled decision tree as <a href="http://bbs.bc-cn.net/post.asp?action=new&boardid=211#3" target="_blank" >Fig. 1</A> shows. The rest of the paper is organized as follows. In Section 2,the symbols will be introduced ﬁrst. In Section 3, the tree construction and data prediction algorithms are described. In Section 4, the experiments are presented. And, ﬁnally, Section 5 makes summaries and conclusions.<o:p></o:p>
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ppm88 发表于 2007-2-19 20:59

好多的单词都懂，但要翻译就需要另外的高人了~帮期待!

sztonyyang 发表于 2007-3-2 22:35

[em09]我绝对没这个能力至少现在没[em02]

ppm88 发表于 2007-3-3 13:50

俺2级都没有过~~ [em08][em08][em08][em08]

songyuyu 发表于 2007-3-6 16:40

找个金山快译试试.虽然不准确的,但对比着翻译应该行

心儿发表于 2007-3-7 11:21

[em05]哈，知道吗在下是 眼睛都看花了

心儿发表于 2007-3-7 11:22

[em03]我什么时候会达到这外成度呢？

liuminghui 发表于 2007-3-7 12:39

弄个下载软件，试

liuminghui 发表于 2007-3-7 12:39

多学点单词吧

carriefeng03 发表于 2007-3-7 14:26

试试在线翻译吧，比较其他的还是可以的，但是需要自己组织整理过，庞大的工程呐

心儿发表于 2007-3-9 09:47

[em05]现在知道自己的才能了吧，英语菜鸟一个哦！ 现在就知道应该多学学英语了吧！[em29]

aidy 发表于 2007-3-14 11:52

如果能够有人告诉我那些我不认识的单词的话 也许我会翻译一点点的.... ..........[em03]

心儿发表于 2007-3-21 16:51

你不可以去找本英语词典吗

limin0753 发表于 2007-3-24 19:53

<a href="http://www.google.com/language_tools" target="_blank" > google语言工具或许可以帮助你 http://www.google.com/language_tools</A>

星梦缘 发表于 2007-3-30 23:25

看来英语课不能睡觉咯．．[em03] 　　[em35]真快，，偶要抓紧咯

心儿发表于 2007-4-18 15:56

是啊，我们就快要踏入社会了，还是多学习一点东西才行呢！[em05]

xt_xl 发表于 2007-4-27 12:12

哎, 难啊 英语真是我等人的悲哀啊

心儿发表于 2007-4-29 13:58

今天刚考完试， 每一次考英语我都不用操心！ 真爽！[em25] 哦，对了大家51节快乐哦！[em29]

songyuyu 发表于 2007-5-10 10:19

看到这么多都怕了

心儿发表于 2007-5-16 15:45

[em05] 英语其实是人的一位很好的朋友。你只要真诚地对待他，我想他也会对你一样的。 加油吧！！！ 相信你自己，你是最棒的！！！！！！！！[em30]

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