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quinlan中文是什么意思

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用"quinlan"造句"quinlan"怎么读"quinlan" in a sentence

中文翻译手机手机版

  • 金兰
  • 昆兰

例句与用法

  • Quinlan . on whose soul sweet jesus have mercy
    仁慈的耶稣,怜悯他的灵魂吧。
  • The jedi quinlan vos is riding on top of a clone turbo tank
    奎南?沃思站在克隆军队的一辆涡轮坦克顶上。
  • The cannon fires , and quinlan vos and the clones disappear in a huge explosion
    大炮轰鸣,奎南?沃思和克隆士兵在猛烈爆炸中化为飞灰。
  • Institute of automation , chinese academy of sciences , 2004 . 10 quinlan j . induction of decisions trees
    换句话说,树表示方法在计算的各个阶段,计算的复杂性与对象个数呈现线性关系。
  • So , even though i knew quinlan was out of the picture , i remained quiet when asked about expanded universe characters appearing in episode iii
    因此,即便我知道奎南已被淘汰出局,当被问到星战iii里的衍生宇宙角色时,我选择了保持沉默。
  • Decision tree is the typical model for classification , and its construction algorithms , that are id3 and c4 . 5 , are proposed by j . r . quinlan in 1986 and 1993 have been well known
    有关分类问题的研究中,较著名的决策树构造算法是j . r . quinlan于1986年和1993年提出的id3和c4 . 5算法。
  • Us companies earned more in japan in 2002 - $ 6 . 8bn in foreign affiliate income - than the combined earnings of us companies ' operations in france and germany , mr quinlan says
    2002年,美国公司在日本获利更多,它们在日本分支机构的收入达68亿美元。这要比美国公司在法国和德国业务的利润总和还多,昆兰先生说。
  • The traditional decision tree , which was represented by id3 proposed by quinlan in 1986 , can solve the classification problem well . but when the class number increases , the produced single decision tree becomes complex and generalization capability decreases
    以quinlan1986年提出的id3为代表的传统决策树能较好的解决分类问题,但当类的个数增多时,所产生的单一决策树就会变得复杂同时概括能力降低。
  • In decision tree algorithm , the famous one is id3 algorithm , which was presented by quinlan in 1986 . it is not a algorithm increasing by degrees , and it uses information entropy as a standard to select attribute , but the disadvantage of this algorithm is that it is easy to select those attributes whose values is more , while attributes whose values is more are not always the best
    在决策树算法中属quinlan于1986年提出的id3算法最有名,它是非递增算法,并且采用信息熵作为属性选择的标准,可是这个标准易偏向于值数较多的属性,而属性值较多的属性却不总是最优的属性。
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