[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f0uJF--ogFfo7l1rOoFckKWUPqzbX8-ZNpd0SeiE9_j8":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2025-06-24 00:41:26",185927387,[8,9,10,11],"支持向量回归模型的解仍然具有稀疏性","间隔带两侧的松弛程度可有所不同","支持向量回归也存在对偶问题","支持向量回归一般要求损失为 0 当且仅当模型的输出和实际值一样",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},48,"8918ca170ab255299558f67cd4aa8a6a","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fda81200f3c2573fd99e5376ca1429a86.jpg","机器学习","work_43205571","第6章 支持向量机","下面关于支持向量回归, 说法错误的是",[21,32,41,51,60,69,78,87,97,106],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],185927369,[25,26,27,28],"对于线性可分的训练样本,存在唯一的超平面将训练样本全部分类正确","间隔只与 w 有关, 与 b 无关","支持向量机训练完成后, 最后的解与所有训练样本都有关","对于线性可分的训练样本, 支持向量机算法学习得到的能哆将训练样本正确分类且具有&quot;最大间隔&quot;的超平面是存在并且唯一的","机基本型中间隔、支持向量和超平面 w x+b=0 的说法,下列说法正确的是","v1",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":30,"type":31},[],185927371,[36,37,38,39],"在推导对偶问题时, 引入的拉格朗日乘子没有约束条件","对偶问题需要满足KKT条件","对偶问题的最优值是原始问题最优值的下界","通过对偶问题推导出的模型表达式能够体现解的稀疏性","下面关于支持向量机对偶问题的说法错误的是",{"answer":42,"createTime":43,"id":44,"options":45,"question":50,"source":30,"type":31},[],"2025-06-24 00:41:27",185927373,[46,47,48,49],"高斯核函数","0\u002F1损失函数","交叉熵函数","指数损失函数","以下属于常用核函数的是( )",{"answer":52,"createTime":5,"id":53,"options":54,"question":59,"source":30,"type":31},[],185927375,[55,56,57,58],"软间隔是支持向量机缓解过拟合的一种手段","采用hinge损失函数后仍保持了SVM解的稀疏性","正则化参数C越小, 模型对分类错误的容忍度越高","软间隔的基本思路为在最大化间隔的同时, 让不满足约束的样本尽可能少","下面有关软间隔支持向量机说法错误的是 ()",{"answer":61,"createTime":43,"id":62,"options":63,"question":68,"source":30,"type":31},[],185927377,[64,65,66,67],"应用核函数","回代求解参数","令偏导数为零","引入拉格朗日乘子","SVM对偶问题求解中,拉格朗日乘子法第二步是( )",{"answer":70,"createTime":5,"id":71,"options":72,"question":77,"source":30,"type":31},[],185927379,[73,74,75,76],"仅适用于线性可分数据","严格等于0\u002F1损失","是0\u002F1损失的上界","非凸且非连续","替代损失函数需满足的条件是( )",{"answer":79,"createTime":5,"id":80,"options":81,"question":86,"source":30,"type":31},[],185927381,[82,83,84,85],"将训练样本分开的超平面仅由支持向量决定","支持向量机的核心思想是最大化间隔","以上选项存在说法错误的","支持向量机基本型是一个凸二次规划问题","下面关于支持向量机的说法错误的是",{"answer":88,"createTime":89,"id":90,"options":91,"question":96,"source":30,"type":31},[],"2025-06-23 23:13:35",185927383,[92,93,94,95],"高维空间的离散点","线性模型的组合","随机森林的集成","核函数的线性组合","表示定理表明,SVM模型可以表示为( )",{"answer":98,"createTime":43,"id":99,"options":100,"question":105,"source":30,"type":31},[],185927385,[101,102,103,104],"无约束优化问题","随机优化问题","有约束优化问题","非凸优化问题","拉格朗日乘子法用于解决( )",{"answer":107,"createTime":5,"id":6,"options":108,"question":19,"source":30,"type":31},[],[8,9,10,11]]