Accession Number : AD1043682


Title :   Budgeted Interactive Learning


Descriptive Note : Technical Report,21 May 2015,20 May 2017


Corporate Author : NATIONAL TAIWAN UNIVERSITY TAIPEI Taiwan


Personal Author(s) : Lin,Hsuan-Tien


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1043682.pdf


Report Date : 15 Jun 2017


Pagination or Media Count : 12


Abstract : This project had two questions it was seeking to answer: how to enrich the protocols for interactive learning?, and how to properly make multi-criteria decisions during the interactive learning process? Towards answering the first question, the PI's team broke it into three subareas:(1) protocols that combine the benefits of online and batch learning, (2) protocols that improve interactive learning with other sources of information, and (3) protocols that allow extracting useful representations during interactive learning. Aligned with the three subareas, they have designed algorithms that allow selecting active learning approaches on the fly (for 2) and transferring the selection experience to other active learning tasks (for 1, 2, and 3). The selection scheme is implemented and released as an open-source active learning package. They have studied theories for designing algorithms for interactive learning with batch-like feedback (for 1) and algorithms for online digestion of representation (for 1 and 3). The team has also addressed real-world needs for considering concept drift during online learning (for 2) and utilizing costs during deep learning, multi-label learning and active learning (for 2 and 3). For the second question, the PI's team has seen promising results on (4) the annotation-budget-sensitive active learning, (5) rethinking deep learning models that trade training/prediction time with performance in large-scale learning, and (6) label embedding models that trade time (embedding length) with performance.


Descriptors :   Machine Learning , artificial neural networks , data mining , deep learning , artificial intelligence , dimensionality reduction , algorithms , sampling , factor analysis , classification , artificial intelligence software


Subject Categories : Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE