classification problem造句
例句與造句
- Discretization of continuous attributes in classification problems
分類問題中連續(xù)屬性的離散化方法 - Improved support vector machine for multi - class classification problems
一種用于多分類問題的改進(jìn)支持向量機(jī) - Usually , the relation extraction problem was converted to a classification problem
通常,將關(guān)系抽取問題轉(zhuǎn)化為一個(gè)分類問題。 - Fuzzy classification systems can deal with perceptual uncertainties in classification problems
模糊分類系統(tǒng)可以處理分類問題中資料的不確定性。 - Because the structure is simple , pnn is an effective model for many classification problems and many pattern recognitions
Pnn的拓?fù)浣Y(jié)構(gòu)簡單,容易設(shè)計(jì)算法,廣泛應(yīng)用于模式識(shí)別及模式分類領(lǐng)域 - It's difficult to find classification problem in a sentence. 用classification problem造句挺難的
- As solving other classification problem , we firstly research on the feature extraction of relation extraction problem , viz
與解決其他分類問題一樣,本文首先對關(guān)系抽取問題中的特征提取進(jìn)行了研究。 - And two types of problems are applied based on the agent - based approach : the nonlinear system approximation problem and the classification problem
最后，基于智能體的進(jìn)化算法補(bǔ)應(yīng)用于非線性系統(tǒng)模擬問題和分類問題中。 - This thesis presents two simple methods to select kernel ' s parameters for classification problems and regression problems respectively . the construction of kernels
本文分別針對模式分類和函數(shù)逼近兩類問題，給出了對核函數(shù)參數(shù)進(jìn)行選擇的簡便方法。 - Lorenz system and mackey - glass time series are the examples for nonlinear system approximation problems , while iris data and wine data are taken as examples for classification problems
分別是二階非線性工廠模型， lorenz系統(tǒng)， mackey - glass時(shí)間序列， iris數(shù)據(jù)集和wine數(shù)據(jù)集。 - Clusen is applied on neural networks to solve the multiple classification problems , the experimental results on uci data sets show that clusen computes more efficiency than gasen proposed before
結(jié)果表明，該技術(shù)計(jì)算效率高，精度與穩(wěn)健性也與基于遺傳算法的選擇性集成方法相當(dāng)甚至占優(yōu)。 - In this thesis , we proposed two methods to construct the membership function for each attribute and to generate fuzzy rules automatically from training instances for handling fuzzy classification problems
在本論文中，我們提出兩種方法來定義各個(gè)屬性的歸屬函數(shù)及產(chǎn)生模糊規(guī)則以解決模糊分類的問題。 - The given method is simple and high efficiency , because most classification problems could be done through the first criterion , and complex solution procedure in getting the object ' s area could be saved
該方法簡單有效，在大多數(shù)情況下用準(zhǔn)則一即可判斷物體形狀，從而避免進(jìn)行較復(fù)雜的目標(biāo)面積的計(jì)算。 - In order to design a fuzzy classification system , it is an important task to construct the membership function for each attribute and generate fuzzy rules from training instances for handling a specific classification problem
在發(fā)展一個(gè)模糊分類系統(tǒng)的過程中，一個(gè)重要的課題是如何定義各個(gè)屬性的歸屬函數(shù)以及產(chǎn)生合適的模糊規(guī)則。 - To prove the performance of methodology , two artificial problems ( one classification problem and one regression problem ) as well as two real problems ( one classification problem and one egression problem ) were employed to verify the methodology
并以二個(gè)人為?值?題（分?與?歸各一題） 、二個(gè)實(shí)際應(yīng)用?題（分?與?歸各一題）加以驗(yàn)證。 - As to its application to multiclass classification problems , most of the methods currently used are based on combining many binary svm classifiers to build a multicalss classifier . in this paper , a new method is proposed
就支持向量機(jī)在模式識(shí)別領(lǐng)域中的多類分類應(yīng)用而言，目前的算法多采用組合兩類支持向量機(jī)分類器進(jìn)行多類分類的方式。
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