Multiple instance learning: A survey of problem characteristics and applications

MA Carbonneau, V Cheplygina, E Granger… - Pattern Recognition, 2018‏ - Elsevier
Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

Co-saliency detection via a self-paced multiple-instance learning framework

D Zhang, D Meng, J Han - IEEE transactions on pattern …, 2016‏ - ieeexplore.ieee.org
As an interesting and emerging topic, co-saliency detection aims at simultaneously
extracting common salient objects from a group of images. On one hand, traditional co …

Deep multi-patch aggregation network for image style, aesthetics, and quality estimation

X Lu, Z Lin, X Shen, R Mech… - Proceedings of the IEEE …, 2015‏ - openaccess.thecvf.com
This paper investigates problems of image style, aesthetics, and quality estimation, which
require fine-grained details from high-resolution images, utilizing deep neural network …

Prototype selection for nearest neighbor classification: Taxonomy and empirical study

S Garcia, J Derrac, J Cano… - IEEE transactions on …, 2012‏ - ieeexplore.ieee.org
The nearest neighbor classifier is one of the most used and well-known techniques for
performing recognition tasks. It has also demonstrated itself to be one of the most useful …

Log-based predictive maintenance

R Sipos, D Fradkin, F Moerchen, Z Wang - Proceedings of the 20th ACM …, 2014‏ - dl.acm.org
Success of manufacturing companies largely depends on reliability of their products.
Scheduled maintenance is widely used to ensure that equipment is operating correctly so as …

Multiple instance learning for classification of dementia in brain MRI

T Tong, R Wolz, Q Gao, R Guerrero, JV Hajnal… - Medical image …, 2014‏ - Elsevier
Abstract Machine learning techniques have been widely used to detect morphological
abnormalities from structural brain magnetic resonance imaging data and to support the …

A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest X-rays

J Melendez, B Van Ginneken… - IEEE transactions on …, 2014‏ - ieeexplore.ieee.org
To reach performance levels comparable to human experts, computer-aided detection
(CAD) systems are typically optimized following a supervised learning approach that relies …

Weakly supervised object detector learning with model drift detection

P Siva, T **
J Wu, S Pan, X Zhu, C Zhang… - IEEE Transactions on …, 2018‏ - ieeexplore.ieee.org
Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning
because it allows a bag of instances to share one label. Bag map** transforms a bag into …