Learning Compact Model for Large-Scale Multi-Label Data
نویسندگان
چکیده
منابع مشابه
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-...
متن کاملOnline Multi-Label Active Learning for Large-Scale Multimedia Annotation
Existing video search engines have not taken the advantages of video content analysis and semantic understanding. Video search in academia uses semantic annotation to approach content-based indexing. We argue this is a promising direction to enable real content-based video search. However, due to the complexity of both video data and semantic concepts, existing techniques on automatic video ann...
متن کاملLarge-scale Multi-label Learning with Missing Labels
The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) scaling up to problems with a large number (say millions) of labels, and (b) handling data with missing labels. In this paper, we directly address both these problems by studying the multi-label problem in a generic empirical r...
متن کاملMulti-resolution Tensor Learning for Large-Scale Spatial Data
High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MRTL, that can significantly speed up the process for spatial tensor models. MRTL leverages the property that spatial data can be viewed at multiple resolutions, which are related by coarsening and finegraining from one resolution to another. Using this property, MRTL learns ...
متن کاملActive Learning Algorithms for Multi-label Data
The iterative supervised learning setting, in which learning algorithms can actively query an oracle for labels, e.g. a human annotator that understands the nature of the problem, is called active learning. As the learner is allowed to interactively choose the data from which it learns, it is expected that the learner would perform better with less training. The active learning approach is appr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33015385