屬于同類規則 的英文怎麼說

中文拼音 [zhǔyútónglèiguīzé]
屬于同類規則 英文
ejusdem generis
  • 屬 : 屬名詞1 （類別） category 2 [生物學] （生物分類系統上所用的等級之一） genus 3 （家屬; 親屬） fami...
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 規 : Ⅰ名詞1 （畫圓形的工具） instrument for drawing circles 2 （規則; 成例） rule; regulation 3 [機械...
  • 則 : Ⅰ名詞1 （規范） standard; norm; criterion 2 （規則） regulation; rule; law 3 （姓氏） a surname Ⅱ...
  • 規則 : 1. （規定的制度或章程） rule; regulation; ordination; prescribed procedure; rope 2. （整齊） regular
  1. We defined a set of rules that correspond to the attributes of the base classes, and then defined the rules for the widgets as specific to each one, which we also did for the base classes rules

    我們定義了一組對應于基類屬性的規則，然後為每一個窗口小部件分別定義了規則，同樣也定義了基類的規則。
  2. When a bnn has been trained, because the learning algorithm for it is various, some binary neurons perhaps belong to a kind of linearly separable ( ls ) series, and some others perhaps belong to another kind of ls series. so it is very significative for extraction of rules from a bnn that the general judging methods and logical meanings of all those ls series are studied

    由於二進神經網路的學習演算法是多種多樣的，因此在一個學習后的二進神經網路中，可能存在不同的神經元屬于不同的幾類線性可分結構系的情況，因此研究二進神經網路中各類線性可分結構系的判別方法和邏輯內涵，對二進神經網路的規則提取是十分有意義的。
  3. An information entropy - based uncertainty measure is presented first based on generalized rough set model in this paper, which is suitable for evaluating rules retrieved from noisy data. second, this paper puts forward generalized minimal - and - maximal - rules - learning methods and generalized maximal - minimal - rules - conversion model because we can encounter noisy problems in most real - life problems. third, this paper puts forward a new discretization method for the continuous attributes, which is based on the clustering and rough sets theory

    本文在對粗集及其相關理論的研究基礎上，首先給出了一種基於推廣粗集模型和信息熵的規則不確定性量度，該不確定性量度適于評價從有噪音數據中提取的規則；鑒于實際應用中經常能遇到噪音的問題，本文提出廣義極小極大規則學習方法，同時還提出了廣義極大極小規則轉換模型gmm ；最後，本文基於聚類方法、結合粗集理論提出了一種新的連續屬性離散化方法。
  4. With known modules, a rule - based fuzzy representation of the module development problem is presented, while the tradeoff of the attributes of performance among modules is analyzed, using a fuzzy neural network approach

    對于已知的模組，使用規則導向的模糊表達方式，來表達模組建構的問題，同時應用模糊類神經的方法，分析模組之間效能相關屬性的取捨。
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