当只有几个正样本,你如何分类无标签数据 假设您有一个交易业务数据集.有些交易被标记为欺诈,其余交易被标记为真实交易,因此您需要设计一个模型来区分欺诈交易和真实交易. 假设您有足够的数据和良好的特征,这似乎是一项简单的分类任务. 但是,假设数据集中只有15%的数据被标记,并且标记的样本仅属于一类,即训练集15%的样本标记为真实交易,而其余样本未标记,可能是真实交易样本,也可能是欺诈样本.您将如何对其进行分类? 样本不均衡问题是否使这项任务变成了无监督学习问题? 好吧,不一定. 此问题通常被称为PU…

PULearning的应用场景是,我们可以清晰地确定正样本,但是不能确定负样本,因为它有可能是正样本,只是我们还没有证明. 这时我们可以把这部分不确定的样本称为无标签样本U,加上正样本P来建立模型. 问题可以转化为一个有约束条件的最优化问题: 在保证正例中错误率低于1-r的条件下,最小化无标签样本中U的正例数目. 建立PU分类器有两种方法: 两步方法two- step approach 直接方法direct approach two-step approach: 第一阶段:从未标记实例中选择可靠…

##Advice for Applying Machine Learning Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the le…

目录 原文链接:小样本学习与智能前沿 01 Transforming Samples from Dtrain 02 Transforming Samples from a Weakly Labeled or Unlabeled Data Set 03 Transforming Samples from Similar Data Sets Discussion and Summary 原文链接:小样本学习与智能前沿 上一篇:A Survey on Few-Shot Learning | Intro…

Chapter 1 Introduction 1.1 What Is Machine Learning? To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algori…

PU learning问题描述 给定一个正例文档集合P和一个无标注文档集U(混合文档集),在无标注文档集中同时含有正例文档和反例文档.通过使用P和U建立一个分类器能够辨别U或测试集中的正例文档 [即想要精确分类U或测试集中的正例文档和反例文档] 应用: 从多个无标注集中学习 从不可靠的反例数据中学习 发现测试集中的突发文档 发现异常值 基于PU-Learning的恶意URL检测 from:https://xz.aliyun.com/t/2190 基于PU-Learning的恶意URL检测 Ya-…

2015年~2017年SIGIR,SIGKDD,ICML三大会议的Recsys论文: [转载请注明出处:https://www.cnblogs.com/shenxiaolin/p/8321722.html] SIGIR-2015 [Title]WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation [Abstract]Matrix approximation…

@http://www-cs-faculty.stanford.edu/people/karpathy/cvpr2015papers/ CVPR 2015 papers (in nicer format than this) maintained by @karpathy NEW: This year I also embedded the (1,2-gram) tfidf vectors of all papers with t-sne and placed them in an interf…

What Is Assertiveness? It's not always easy to identify truly assertive behavior. This is because there's a fine line between assertiveness and aggression, and people can often confuse the two. For this reason, it's useful to define the two behaviors…

标签(空格分隔): 毕业论文 (OS: 最近在做关于多类分类的综述,但是搜索出来好多方向搞得自己云里雾里的,好吧,又是在下孤陋寡闻了.还是那句话,不知道不可怕,但一直不知道就很尴尬了.) one-class classification -- 一元分类 In machine learning, one-class classification, also known as unary classification, tries to identify objects of a specific…