樹分類 的英文怎麼說
中文拼音 [shùfēnlèi]
樹分類
英文
tree sort-
On the basis of analyzing the classification principle of decision tree classifier and parallelpiped classifier, a new classification method based on normalized euclidian distance, called wmdc ( weighted minimum distance classifier ), was proposed
通過分析多重限制分類器和決策樹分類器的分類原則，提出了基於標準化歐式距離的加權最小距離分類器。Compared with the classical model of decision tree, this model has more excellences such as being easy to build the model and to expand, a great capability of fault tolerance
通過與經典的決策樹分類模型進行比較，本文分類方案具有建模簡單、擴展性好、容錯能力強等優點。Studies on decisiontree classifying method in dataming
數據挖掘中決策樹分類方法研究This thesis explains the necessity of the character recognition technology of the computer at first, describe the meaning in which the handwritten numeral discerns ; pretreatment technology of handwritten numeral recognition, including two value, line segmentation, word segmentation smooth, removing noising, standardization and thinning are discussed two value concretely discusses whole threshold value, some threshold value, dynamic threshold value and utilize space information to carry on threshold, which are several kinds of common method of choosing threshold value, especially utilize space information to carry on threshold value is describe in detail ; adopting to the foundation of thinning based on mathematics morphology, thinning algorithm of serials same and thinning algorithm of protecting shape are discussed ; afterwards, according to principle ' s diagram of the on - line character recognition, by analyzing the structure feature of the handwritten numeral, this thesis has proposed the online recognition te chnology of the free handwritten numeral based on the stroke feature and the online recognition technology of the free handwritten numeral based on the multistage classifying device. detail narrated noise removing, stroke characteristic definition and discernment, distance criterion of whole word match ; then under the foundation of handwritten numeral segmentation, off - line handwritten numeral recognition is researched. especially minimum distance classifying device, tree classifying device and adaptive resonance ( art ) network classifying device is discussed at the same time, believes degree analyses are introduced to integrate a lot of classifying devices ; at the end, the typical application of the handwritten numeral recognition was briefly narrated, its application in extensive data statistics, financial affairs, tax, finance and mail sorting have been explored
二值化時對整體閾值二值化、局部閾值二值化、動態閾值二值化和利用空間信息進行閾值選取幾種常用的閾值選取方法進行討論，特別對利用空間信息進行閾值選取進行了詳細論述；在對通過對基於數學形態學的細化的基礎上，討論序貫同倫形態細化演算法和保形的快速形態細化演算法；然後依據聯機字元識別原理框圖，分析了手寫數字的結構特點，提出了基於筆劃特徵的任意手寫數字在線識別技術和基於多級分類器任意手寫數字在線識別技術，對其中涉及的筆劃識別前的噪聲處理、筆劃間特徵量的定義及識別、整字匹配的距離準則進行了詳細敘述；繼而在對手寫數字的分割的基礎下對脫機手寫數字識別進行了研究，對基於最小距離分類器字元識別、基於樹分類器的字元識別、基於自適應共振（ art ）網路的字元識別分別進行了詳細討論，並引入置信度分析將多個分類器進行了混合集成；最後簡單闡述了手寫數字識別的典型應用，對其在大規模數據統計、財務、稅務、金融及郵件分揀中的應用進行了探索。Decision tree classification algorithm based on bayesian method
基於貝葉斯方法的決策樹分類演算法In this paper, the decision tree classify algorithm is choosed as the emphasis
選取決策樹分類演算法為研究重點。This paper is a study on decision tree classification algorithms, which mainly includes two parts
本文主要對決策樹分類演算法展開研究，主要包含兩個內容： 1The traditional decision tree category methods ( such as : id3, c4. 5 ) are effective on small data sets
摘要傳統的決策樹分類方法（如id3和c4 . 5 ）對于相對小的數據集是很有效的。So bp neural network is used to classify the stored food insects primarily. the result shows our method is effective
實驗表明，利用bp神經網路分類器作初級分類器的樹分類結構進行分類，是一種行之有效的方法。This new idea can be applied for any other algorithm. thus it produces a new way for improving classifying algorithm accuracy
這種方法也可以應用於其它的決策樹構造演算法中，為提高決策樹分類演算法的準確性提供了新的途徑。Many different techniques have been proposed for classification, including statistical approaches, neural networks, decision tree algorithm and rough sets
現有數據分類方法有統計方法、決策樹分類方法、神經網路方法、粗集法等。Secondly, decision tree classification model and logistic regression model are performed to rock mass quality assessment, based on sas / enterprise miner
應用sas enterpriseminer系統的決策樹分類演算法和logistic回歸演算法進行巖體的質量分級評價。In this paper, based on the comprehension of the current research situation, we mainly discussed the problem how to adapt the decision tree in common use to the large scale dataset
針對大數據量、多屬性值的情況，對決策樹分類器所需屬性信息的求解提出了新的改進演算法。An attribute means clustering binary tree is presented in this paper. the binary tree is extends naturally and turns to be a supervised classification method. the orl database is used to evaluate the proposed method. the performance of the attribute means clustering binary tree used in face recognition is compared with the standard eigenface approach and improves their performance much in the experiment
在無監督的屬性聚類網路的基礎上，提出了一種二叉樹分類方法。此二叉樹自然地在無監督聚類的基礎上擴展開來，成為一有監督的分類方法。用orl人臉數據庫做了測試，同標準的特徵臉eigenface方法相比，識別率得到了較大的提高。At the stage of image recognition, a unique model of pcb fault recognition was built on methods of tree - classification and sequenntial probability ratio test, and a kind of method of m feature selection and extraction was introduced
在圖像識別中，本文分析了常用的模式識別方法，根據樹分類法和序貫概率比檢定法的思想設計了一種獨特的pcb缺陷模式識別方法；並給出了針對各種pcb缺陷模式的特徵選擇與提取方法。Judgment - tree method has been applied to solve lots of categorized problems successfully but so far in china it has never been applied into classification and prediction about customers " behavior of banks
判定樹分類法已被成功地應用於許多分類問題，但應用於銀行的客戶行為分類預測研究在國內到目前為止還沒有。A further study has been made about decision tree classification, bayesian network, and discretization of conntinuous attributes, at the same time many kinds of classfication algorithms have been achieved
對決策樹分類、貝葉斯網路和連續屬性的離散化問題進行了的研究，實現了多種分類演算法。When we design the classification, we combine the tree classification and the support vector machines in order to improve the ability of combining experiences and performance of generalization
在模式識別的分類器設計上，我們採用了樹分類器和支持向量機相結合的方法，提高了分類器經驗結合的能力和泛化能力。We also make plenty of classification experiments with data sets from various of different fields, and then analyse and compare the classification capacity of several decision tree classification algorithms and the adaptability to different datas
在來自不同領域的數據集上進行了大量的分類實驗，分析和比較了多種決策樹分類演算法的分類性能和對不同數據的適應性。The algorithm of sf _ dt, which bases on the idea of decision tree classification algorithm ids, use the means of file splitting take the place of the means which bases on memory. it improves the scalability of classification algorithm and can deal with very large database
Sf _ dt演算法以決策樹分類演算法id3的基本思想為基礎，用基於文件分割的方法代替原有的基於內存的演算法，提高了演算法的可規模性，可以處理超大規模的數據。分享友人