图 书 馆
模型订单选择的最小转移成本原则The Minimum Transfer Cost Principle for Model-Order Selection |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_haghir_chehreghani_principl... |
| 主讲教师: | Morteza Haghir Chehreghani |
| 开课单位: | 苏黎世理工学院 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  模型顺序选择的目的是选择一种模型变体，该模型变体从训练数据到看不见的测试数据最好地泛化。在没有标签的无监督学习中，解决方案的泛化误差的计算提出了一个概念性的问题，我们将在本文中解决。我们为模型订单的选择制定了“最小转移成本”的原则。该原理使交叉验证的概念适用于无监督学习问题。作为标签的替代，我们在训练集的对象到测试集的对象之间引入了映射，从而可以传输训练解决方案。通过将其应用到众所周知的问题（例如奇异值分解，相关性聚类，高斯混合模型和k均值聚类）来解释和研究我们的方法。我们的原则是在受控实验以及现实世界中的问题（例如图像去噪，角色挖掘和访问控制数据中的错误配置检测）中找到最佳模型复杂性。 |
| 课程简介: | The goal of model-order selection is to select a model variant that generalizes best from training data to unseen test data. In unsupervised learning without any labels, the computation of the generalization error of a solution poses a conceptual problem which we address in this paper. We formulate the principle of "minimum transfer costs" for model-order selection. This principle renders the concept of cross-validation applicable to unsupervised learning problems. As a substitute for labels, we introduce a mapping between objects of the training set to objects of the test set enabling the transfer of training solutions. Our method is explained and investigated by applying it to well-known problems such as singular-value decomposition, correlation clustering, Gaussian mixturemodels, and k-means clustering. Our principle finds the optimal model complexity in controlled experiments and in real-world problems such as image denoising, role mining and detection of misconfigurations in access-control data. |
| 关 键 词: | 模型变体; 数集映射 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-20：zyk |
| 最后编审: | 2021-03-20：zyk |
| 阅读次数: | 56 |