图 书 馆
机器学习与视觉中的子模块化Submodularity in Machine Learning and Vision |
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| 课程网址: | http://videolectures.net/bmvc2013_krause_machine_learning/ |
| 主讲教师: | Andreas Krause |
| 开课单位: | 苏黎世理工学院 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  机器学习和视觉领域的许多问题本质上都是离散的。通常，这些会导致具有挑战性的优化问题。虽然凸性是解决连续优化问题的重要属性，但子模块性通常被视为凸性的离散模拟，是解决许多离散问题的关键。它的特征属性，边际收益递减，自然地出现在多种环境中。虽然亚模块性早已在组合优化和博弈论中得到认可，但最近人们对理论计算机科学、机器学习和计算机视觉的兴趣激增。本教程将介绍子模块性的概念及其基本属性，并概述最近的研究方向，例如大规模优化和顺序决策任务的新方法。我们将讨论最近在具有挑战性的机器学习和视觉问题上的应用，例如高阶图形模型推理、结构化稀疏建模、多目标检测、主动感知等。本教程不会假设任何有关该主题的特定先验知识。 |
| 课程简介: | Numerous problems in machine learning and vision are inherently discrete. More often than not, these lead to challenging optimization problems. While convexity is an important property when solving continuous optimization problems, submodularity, often viewed as a discrete analog of convexity, is key to solving many discrete problems. Its characterizing property, diminishing marginal returns, appears naturally in a multitude of settings. While submodularity has long been recognized in combinatorial optimization and game theory, it has seen a recent surge of interest in theoretical computer science, machine learning and computer vision. This tutorial will introduce the concept of submodularity and its basic properties, and outline recent research directions -- such as new approaches towards large-scale optimization and sequential decision making tasks. We will discuss recent applications to challenging machine learning and vision problems such as high-order graphical model inference, structured sparse modeling, multiple object detection, active sensing etc. The tutorial will not assume any specific prior knowledge on the subject. |
| 关 键 词: | 凸性离散模拟; 图形模型推理; 理论计算机科学 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-23：zyk |
| 最后编审: | 2021-06-24：liyy |
| 阅读次数: | 53 |