Paper Info
Title
Analysis of a Design Pattern for Teaching with Features and Labels.
Abstract
We study the task of teaching a machine to classify objects using features and labels. We introduce the Error-Driven-Featuring design pattern for teaching using features and labels in which a teacher prefers to introduce features only if they are needed. We analyze the potential risks and benefits of this teaching pattern through the use of teaching protocols, illustrative examples, and by providing bounds on the effort required for an optimal machine teacher using a linear learning algorithm, the most commonly used type of learners in interactive machine learning systems. Our analysis provides a deeper understanding of potential trade-offs of using different learning algorithms and between the effort required for featuring (creating new features) and labeling (providing labels for objects).
Year
Venue
Field
2016
arXiv: Artificial Intelligence
Computer science,If and only if,Artificial intelligence,Machine learning,Design pattern
DocType
Volume
Citations 
Journal
abs/1611.05950
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Christopher Meek162.77
Patrice Simard21268621.43
Xiaojin Zhu33586222.74