Results 251 to 257 of about 1,256,985 (257)

Ensemble Extreme Learning Machine for Multi-instance Learning

Proceedings of the 9th International Conference on Machine Learning and Computing, 2017
Multi-instance learning (MIL) is a classification approach for classifying on a collection of instances which each group is represented as a bag. The main task of MIL is to learn from labels and features of instances to produce a model to predict a label of a testing bag.
Songpon Sastrawaha, Punyaphol Horata
openaire   +1 more source

Fast Multi-Instance Partial-Label Learning

Proceedings of the AAAI Conference on Artificial Intelligence
Multi-instance partial-label learning (MIPL) is a paradigm where each training example is encapsulated as a multi-instance bag associated with the candidate label set, which includes one true label and several false positives. Current MIPL algorithms typically assume that all instances are independent, thereby neglecting the dependencies and ...
Yin-Fang Yang, Wei Tang, Min-Ling Zhang
openaire   +1 more source

Multi-Instance Learning from Supervised View

Journal of Computer Science and Technology, 2006
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners ...
openaire   +1 more source

Bag dissimilarity regularized multi-instance learning

Pattern Recognition, 2022
Shiluo Huang, Zheng Liu, Wei Jin, Ying Mu   +3 more
openaire   +1 more source

Cost‐effective multi‐instance multilabel active learning

International Journal of Intelligent Systems, 2021
Cong Su, Zhongmin Yan, Guoxian Yu
openaire   +1 more source

Multi-Instance Learning

2012
openaire   +1 more source

Learning and Interpreting Multi-Multi-Instance Learning Networks

2019
openaire   +2 more sources