training classifier 中文意思是什麼

training classifier 解釋
訓練分類器
  • training : n 訓練，教練，練習；鍛煉；（馬等的）調馴；（槍炮、攝影機等的）瞄準，對準；【園藝】整枝法。 be in ...
  • classifier : n. 1. 分類者。 2. 【礦物】分級機。3. 【化學】分粒器。4. （漢語等中的）量詞。
  1. Mining classification rules is a procedure to construct a classifier through studying the training dataset. it is a very important part of data mining and knowledge discovery

    分類規則挖掘則是通過對訓練樣本數據集的學習構造分類規則的過程，是數據挖掘、知識發現的一個重要方面。
  2. The output information of single classifier has three forms of abstract, rank and measurement single classifier supplies both the unknown pattern classifying information on the measurement level and the wrong classifying distribution information of the training samples on the abstract level, which are used to design the fuzzy multiple classifiers combination method

    單個分類器的輸出信息有三種表現形式：符號層、排序層、度量層。應用單個分類器在度量層次上，對未知模式的分類信息；在符號層次上，訓練樣本的錯分類分佈狀況，設計了模糊多分類器組合方法。
  3. 4 ) semantic discovering and matching model based bp network the classifier output is used as training data for a bp neural net. the net produced by this can recognize attributes within the database based on their metadata and emerge learning rules

    4 ）基於bp網路的語義發現和匹配模型設計建立bp網路模型，通過對樣本數據進行學習進而形成匹配規則，用於異構數據庫之間的語義匹配。
  4. The algorithms of text classification are supervised, which means the classifier training need some human labeled data of fixed classes. generally, the accuracy of classifier is higher with more labeled data. but the labeled data by hand are expensive resource

    文本分類演算法是有監督的學習演算法，它需要一個分類好的，類別已標識的文本數據集訓練分類器，然後用訓練好的分類器對未標識類別的文本分類。
  5. In the practice stage, an integrated classifier is trained for the current user, whose training set is composed of genuine signatures, and random signatures selected from genuine signatures of existed users registered in the system

    在系統使用階段，對每個用戶，其訓練集中包含該用戶的真實簽名，並抽取系統中已有用戶的真實簽名作為隨機偽造，訓練組合分類器。
  6. A decision tree classifier using a scalable id3 algorithm is developed by microsoft visual c + + 6. 0. some actual training set has been put to test the classifier and the experiment shows that the classifier can successfully build decision trees and has good scalability

    最後著重介紹了作者獨立完成的一個決策樹分類器。它使用的核心演算法為可伸縮的id3演算法，分類器使用microsoftvisualc + + 6 . 0開發。
  7. Instead of fix grids, adaptive grids derived from the pixel projection histograms are employed. the proposed system ensembles multiple classifiers based on boosting algorithm. this integrated classifier works in a similar way to the serial integration mode in the training stage, and to the parallel integration mode in the classification stage

    實驗基礎上，本文從全局特徵集、紋理特徵集中選取分類能力強的特徵分量集；並改進網格的選取方案，採用基於像素投影直方圖的自適應網格劃分，以處理用戶簽名的多變性。
  8. Then this paper presents a text classifier based on neural networks ( nntcs ) as the main topic. some key techniques implemented in this classifier, such as feature extraction, dimension reduction, hierarchical classification and classifier training, are discussed in details

    然後著重介紹了一個基於神經網路的文本自動分類系統nntcs ，重點闡述了特徵提取、空間降維、層次分類和分類器訓練等技術的實現方法。
  9. The construction of input and output sample, designing of classifier and designing of the training network configuration are introduced respectively, some improvement algorithms of bp neural network are discussed. the efficiency of star pattern recognition greatly is improved through ameliorating configuration of network and program method

    介紹了識別系統中輸入、輸出樣本的構造，分類器的設計以及訓練網路結構的設計，討論了基於bp神經網路訓練的一些改進演算法，通過對網路結構與編程方法的改進使星圖識別的效率大大提高。
  10. This paper studied the method of gene selection and four parts work are studied as follows : ( l ) present a gene selection method by training linear svm ( support vector machine ) classifier and testing them with cross validation for finding gene subset which is optimal / suboptimal for diagnosis of binary disease classes

    本文針對dna微陣列數據，進行了基因選擇方面的研究。本文主要做了以下四部分的工作： （ 1 ）提出了實現二病類樣本有效分類的基於svm + leave - one - out遞增基因選擇方法。
  11. This paper organizes as follows : chapter 1 mainly introduces the research background, and gives a description of the research content of this paper after discusses the research trend on classification in dataming ; chapter 2 focuses on the theory of cart and gives a algorithm which integrates the tree building phase and pruning phase ; chapter 3 gives a detailed description on rbf and proposes the center selection method based on the statistic information of the training dataset. chapter 4 is the design and realization of the classifier. and chapter 5 summarize the work we i done and analyses the work to do later and obtain the research subject in the future

    本論文的組織結構為：第一章為緒論，介紹了研究背景和分類分析研究領域國內外的研究動態，闡述了本文的主要研究內容；第二章詳細敘述了cart分類器的原理，給出了建樹與修剪階段合併的演算法；第三章描述了rbf神經網路分類器，給出了基於樣本統計的網路中心選取方法；第四章講述了cart分類器的設計與實現；第五章對本課題的研究工作進行了總結，分析了今後所要進行的工作以及進一步研究的課題。
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