谢谢了。
那个，见到伯父、阿姨，我要说什么？吕馨有些不好意思的说道。
Wang Zeduan previously described that the blood-like liquid flowing out of this strange dog after being hit by steel balls produced by anti-infantry mine explosions is also green, Combined with what Zhao Mingkai said now, It can be determined that for this strange dog, Just as blood represents the primary color of human body fluids in red, The primary color of their body fluids is green, And more unified than human beings, After all, the human brain solution is milky white, Unlike blood, However, whether they are blood or brain solution (in fact, I don't know if it is their blood or brain solution, so I used the word "image" to describe them in front. After all, whether they are natural or artificial, they belong to unknown species, but for convenience of description, they are all green.
衍生剧《BORDER 冲动~检视官比嘉美香~》的主人公是波瑠饰演的法医学教室助手比嘉美香，在日剧版中，她的女汉子形象以及超乎寻常的验尸能力为她博得了人气。 比嘉美香是警视厅特别检视官，帮助主人公石川一起调查案件，悬疑剧时间设定在她就任特别检视官半年前，那时她还在永正大学医学部医学教室担任助手，帮助解决震惊日本的连续杀人案，为了破案她甚至与老师法医学教授对峙，用自己的信念、正义和细致解决问题。
还真是出乎人意料啊。
一位不速之客的意外到访，打破了神油店老板程勇的平凡人生，他从一个交不起房租的男性保健品商贩，一跃成为印度仿制药“格列宁”的独家代理商。收获巨额利润的他，生活剧烈变化，被病患们冠以“药神”的称号。但是，一场关于救赎的拉锯战也在波涛暗涌中慢慢展开......
Editor of Wang Yuqian Henan Eye Cancer Girl Series Report: Wang Qiang Liu Haichuan
讲汽车
今儿哪怕把这事赖到玉米和花生头上，也不能让人知道是苞谷干的。
虞子期神情平复下来，见到妹妹平安无事也就放心了，说道：霸王，末将前来也是有消息回禀。
祭酒是正官啊……李天宠皱眉道，此人可有功名？没提。
Independent Attack +45
故事发生在英格兰西南部的多塞特郡一个安宁的海滨小镇Broadchurch。满身血污的DannyLatimer的尸体被发现在一片风景宁静的海滩上。这座安逸的滨海小镇因此被推上了风口浪尖，不仅引来了居民们的惊恐，还招致媒体的疯狂报道。新获得提升的侦探巡官AlecHardy被调到这座小镇任职，因为此举抢了本属于当地女侦探EllieMiller的职位，因而遭到她的敌视。面对这起意外死亡，两人不得不联手共同调查……
太乙仙魔录是由糖心传媒出品、舞之动画制作的首部3D仙侠动漫系列作品，原著作者于彬（函谷生）。《太乙仙魔录之灵飞纪》讲述青城派女弟子冷霜凝天资卓绝，本应继承掌门之位，因修行受阻，问道于青玉坛。在意外得知自己前世与昆仑二次决战有关之后，为解开前世之谜，找到命盘所示的改命人，独自一人离开青城山。偶然窥见魔教阴谋，并结识一众命定之人，由此展开一段仙侠奇幻之旅……
Laura是一个幸福的女人，她有着爱她的丈夫Tom和可爱的女儿Serena。然而一天，女儿Serena被人在水里发现，伤痛不已的Laura决定投河自尽随女儿而去。然而，Laura投河后却回到了两周前，她拼命想要扭转女儿被谋杀的命运，然而随着对女儿的保护，她也离谋杀的真相越来越近……
叶家姐妹叶依姗和叶依婷与母亲冯代珍，靠经营祖传瓦罐汤小店生活。姐姐依姗勤勉善良，妹妹依婷虚荣善妒，依姗马上要跟大集团继承人秦明昊订婚，依婷接受不了自己的失败、姐姐的成功，开车带着依姗狂奔，酿成车祸，姐妹俩双双毁容、昏迷。
他一把掐住她脖子，她就一低头咬住他手腕。
Considering N categories C1, C2 …, CN, the basic idea of multi-classification learning is "disassembly method", that is, multi-classification tasks are disassembled into several two-classification tasks to solve. Specifically, the problem is split first, and then a classifier is trained for each split second classification task. During the test, the prediction results of these classifiers are integrated to obtain the final multi-classification results. The key here is how to split multiple classification tasks and how to integrate multiple classifiers.
This is the learning method adopted by most people at present, and it is basically the same as nine.
