Open AccessMultiple-instance Learning based on Bernoulli Mixture Model
Author(s) -
Yang Liu,
Wei Wu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1650/3/032071
Subject(s) - bernoulli's principle , construct (python library) , artificial intelligence , computer science , machine learning , process (computing) , artificial neural network , pattern recognition (psychology) , engineering , aerospace engineering , programming language , operating system
Multiple-instance learning (MIL) is a form of weakly supervised learning. Its instances are arranged in groups (called bags), and labels are provided for the entire bag after training. The classic MIL method represents examples with pre-calculated features, and the classification process is also cumbersome. In this paper, we use neural networks to extract features, parameterize all transformations, and use Bernoulli mixture model to construct MIL models for baggage tags, using simpler network structures and more accurately solving these problems. Experiments show that our results can be competitive with the classical MIL algorithm on the MINST dataset.