[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fUiccU0R6meHLrVTRW7RI6-4wQndh4HlM14HMEBRJim0":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":16,"related":17,"source":27,"type":28},[],"2023-12-30 21:04:58",123370622,[8,9,10,11],"假设空间","版本空间","输出空间","输入空间",{"courseId":13,"courseImg":14,"courseName":15},"15706eb2a9c7ceda1f4403a1c0353f39","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F9311a73ad2b70ba131662b89e7ebd774.png","机器学习","现实问题中我们常面临很大的假设空间,但学习过程是基于有限样本训练集进行的,因此,可能有多个假设与训练集一致,即存在着一个与训练集一致的"假设集合",我们称之为( )",[18,29,35,45,55,64,73,82,91,94],{"answer":19,"createTime":5,"id":20,"options":21,"question":26,"source":27,"type":28},[],123370608,[22,23,24,25],"对数几率函数","对数线性函数","Sigmoid 函数","单位阶跃函数","对数几率回归的联系函数是( )","v1",0,{"answer":30,"createTime":31,"id":32,"options":33,"question":34,"source":27,"type":28},[],"2023-12-30 21:04:57",123370609,[9,8,10,11],"反映事件或对象在某方面的表现或性质的事项,称为"属性";属性上的取值,称为"属性值";属性张成的空间称为( )",{"answer":36,"createTime":37,"id":38,"options":39,"question":44,"source":27,"type":28},[],"2023-12-30 12:07:55",123370612,[40,41,42,43],"C4.5算法","标准BP算法","累积BP算法","ID3算法","( )每次更新只针对单个样例,参数更新的非常频繁,而且对不同样例进行更新的效果可能出现"抵消"现象",{"answer":46,"createTime":47,"id":48,"options":49,"question":54,"source":27,"type":28},[],"2023-12-28 19:20:03",123370613,[50,51,52,53],"以上都是","非","与","或","感知机由两层神经元组成,输入层接收外界输入信号后传递给输出层,输出层是M-P神经元,亦称"阈值逻辑单元".它能容易的实现逻辑( )运算",{"answer":56,"createTime":31,"id":57,"options":58,"question":63,"source":27,"type":28},[],123370615,[59,60,61,62],"连接主义学派","频率主义学派","符号主义学派","贝叶斯学派","( )认为参数虽然未知,但却是客观存在的固定值,因此,可通过优化似然函数等准则来确定参数值",{"answer":65,"createTime":5,"id":66,"options":67,"question":72,"source":27,"type":28},[],123370616,[68,69,70,71],"误差逆传播算法","CART决策树算法","C4.5决策树算法","ID3决策树算法","给定划分属性,若样本在该属性上的值缺失,则让该样本以不同的概率划入到不同的子结点中去. ( )使用了上述解决方案",{"answer":74,"createTime":47,"id":75,"options":76,"question":81,"source":27,"type":28},[],123370617,[77,78,79,80],"基尼指数","增益率","信息增益","信息熵","著名的C4.5决策树学习算法就是以( )为准则来选择划分属性",{"answer":83,"createTime":37,"id":84,"options":85,"question":90,"source":27,"type":28},[],123370618,[86,87,88,89],"点估计","面估计","概率估计","极大似然估计","源自频率主义学派的( ),是根据数据采样来估计概率分布参数的经典方法",{"answer":92,"createTime":5,"id":6,"options":93,"question":16,"source":27,"type":28},[],[8,9,10,11],{"answer":95,"createTime":37,"id":96,"options":97,"question":102,"source":27,"type":28},[],123370623,[98,99,100,101],"p\u002F(1-p)","p","(1-p)\u002Fp","1-p","假设某事件发生的概率为p,则此事件发生的几率为( )"]