网络多示例多标记学习 网络释义 1. 多示例多标记学习 ...标记.针对这个目的,笔者提出了 MIML——即"多示例多标记学习" (Multi-Instance Multi-Label learning)这一学习框架[1][2].本章 … wenku.baidu.com|基于3个网页
Multi-Instance Multi-Label learning (MIML) is a popular framework for supervised classification where an example is described by multiple instances and associated with multiple labels. Previous MIML approaches have focused on predicting labels for instances. The idea of tackling the problem is to ...
In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated ob...
为解决多标记新标记学习问题, 本文提出了一种端到端的多视图多示例标记新标记学习方法EM3NL (end-to-end multi-view multi-instance multi-label learning with new labels), 将深度神经网络与多视图、多示例多标记学习结合, 输入原始图片和文本, 输出已知标记和新标记的预测. 本文主要贡献如下: ...
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associated with a list of objects it contains. Prior...
Multi-instance multi-label learning (MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels. During the past few years, many MIML algorithms have been developed and many applications have been described. However, ...
1、multilabel classification的用途 多标签分类问题很常见, 比如一部电影可以同时被分为动作片和犯罪片, 一则新闻可以同时属于政治和法律,还有生物学中的基因功能预测问题, 场景识别问题,疾病诊断等。 2. 单标签分类 在传统的单标签分类中,训练集中的每一个样本只有一个相关的标签 l ,这个标签来自于一个不重合的...
Multi-Instance Multi-Label Learning - 南京大学多示例多标记学习南京大学 热度: 基于多示例学习的抗遮挡目标跟踪算法研究 热度: 基于贝叶斯网络的多示例学习算法研究 热度: 多样性密度(diversedensity)方法,在多示例学习的 应用研究领域最具影响力。多示例学习问题也能同时 ...
1.什么是multi-instance learning? 1.1 定义 multi-instance learning MIL的数据集的数据的单位是bag,以二分类为例,一个bag中包含多个instance,如果所有的instance都被标记为negative,那么这个包就是negative,反之这个包为positive。 设Y为包X的label,X={x1,x2,...,xn},每个示例xi对应一个标签yi,则...
Because of this, traditional supervised learning, which assumes that each example is explicitly mapped to a label, is not appropriate. We propose a novel approach to multi-instance multi-label learning for RE, which jointly models all the instances of a pair of entities in text and all their...