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題名 預測誤差及變異效應之估計與分類
其他題名 Effect of Bias and Variance on Estimation and Classification Error for Prediction
作者 朱建平;鄭宇庭
Rahman, Mostafizur ; Zhu, Jian-Ping ; Cheng, Yu-Ting
日期 2006-06
上傳時間 19-Dec-2008 14:44:56 (UTC+8)
摘要 我們的目標是呈現出預測平方誤差與分類誤差的變異以及誤差的影響。我們發現估計平方誤差與分類誤差的變異以及偏誤是不同的。對於給予的誤差／變異，估計平方誤差是與各變異與誤差對稱的。分類誤差則是依誤差範圍的量而定。若誤差範圍是負的，那麼分類誤差則會不論預測誤差是否增加而減少。誤差範圍是正的情況下，分類誤差會增加預測距離的1/2。誤差範圍的影響在分類誤差中可以因小的變異而減少。相似的變異誤差則視誤差範圍的值而定。我們使用最方便的方法來使這些影響降至最小。
Our aim is to show the effect of bias and variance on squared estimation error and classification error. We found that the bias and variance affect squared estimation error and classification error differently. For a given bias/ variance, squared estimation error is proportional to variance/ bias respectively. Classification error depends on the relevant quantity of boundary bias. If the boundary bias is negative then classification error decreases with increasing irrespective of the estimation bias. For positive boundary bias the classification error increases with the distance of estimation from 1/2. The effect of boundary bias on classification error can be mitigated by low variance. Similarly the affect of the variance depends on the value of the boundary bias. And we use nearest neighbor methods for minimizing these effects.
關聯 數據分析= Journal of Data Analysis, 1(3),113-129
資料類型 article
dc.creator (作者) 朱建平;鄭宇庭zh_TW
dc.creator (作者) Rahman, Mostafizur ; Zhu, Jian-Ping ; Cheng, Yu-Ting-
dc.date (日期) 2006-06en_US
dc.date.accessioned 19-Dec-2008 14:44:56 (UTC+8)-
dc.date.available 19-Dec-2008 14:44:56 (UTC+8)-
dc.date.issued (上傳時間) 19-Dec-2008 14:44:56 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/18074-
dc.description.abstract (摘要) 我們的目標是呈現出預測平方誤差與分類誤差的變異以及誤差的影響。我們發現估計平方誤差與分類誤差的變異以及偏誤是不同的。對於給予的誤差／變異，估計平方誤差是與各變異與誤差對稱的。分類誤差則是依誤差範圍的量而定。若誤差範圍是負的，那麼分類誤差則會不論預測誤差是否增加而減少。誤差範圍是正的情況下，分類誤差會增加預測距離的1/2。誤差範圍的影響在分類誤差中可以因小的變異而減少。相似的變異誤差則視誤差範圍的值而定。我們使用最方便的方法來使這些影響降至最小。-
dc.description.abstract (摘要) Our aim is to show the effect of bias and variance on squared estimation error and classification error. We found that the bias and variance affect squared estimation error and classification error differently. For a given bias/ variance, squared estimation error is proportional to variance/ bias respectively. Classification error depends on the relevant quantity of boundary bias. If the boundary bias is negative then classification error decreases with increasing irrespective of the estimation bias. For positive boundary bias the classification error increases with the distance of estimation from 1/2. The effect of boundary bias on classification error can be mitigated by low variance. Similarly the affect of the variance depends on the value of the boundary bias. And we use nearest neighbor methods for minimizing these effects.-
dc.format application/pdfen_US
dc.format.extent 129713 bytes-
dc.format.mimetype application/pdf-
dc.language zh-TWen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) 數據分析= Journal of Data Analysis, 1(3),113-129en_US
dc.title (題名) 預測誤差及變異效應之估計與分類zh_TW
dc.title.alternative (其他題名) Effect of Bias and Variance on Estimation and Classification Error for Prediction-
dc.type (資料類型) articleen