[HTML][HTML] Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction
J Zhao, J Ouenniche, J De Smedt - Machine Learning with Applications, 2024 - Elsevier
Corporate bankruptcy and financial distress prediction is a topic of interest for a variety of
stakeholders, including businesses, financial institutions, investors, regulatory bodies …
stakeholders, including businesses, financial institutions, investors, regulatory bodies …
Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches
Risk stratification (characterization) of tumors from radiology images can be more accurate
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …
Easy learning from label proportions
We consider the problem of Learning from Label Proportions (LLP), a weakly supervised
classification setup where instances are grouped into iid “bags”, and only the frequency of …
classification setup where instances are grouped into iid “bags”, and only the frequency of …
Learning from aggregate observations
Y Zhang, N Charoenphakdee, Z Wu… - Advances in Neural …, 2020 - proceedings.neurips.cc
We study the problem of learning from aggregate observations where supervision signals
are given to sets of instances instead of individual instances, while the goal is still to predict …
are given to sets of instances instead of individual instances, while the goal is still to predict …
Negative pseudo labeling using class proportion for semantic segmentation in pathology
H Tokunaga, BK Iwana, Y Teramoto… - Computer Vision–ECCV …, 2020 - Springer
In pathological diagnosis, since the proportion of the adenocarcinoma subtypes is related to
the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for …
the recurrence rate and the survival time after surgery, the proportion of cancer subtypes for …
A stochastic expectation-maximization approach to shuffled linear regression
We consider the problem of inference in a linear regression model in which the relative
ordering of the input features and output labels is not known. Such datasets naturally arise …
ordering of the input features and output labels is not known. Such datasets naturally arise …
Learning with label proportions via NPSVM
Recently, learning from label proportions (LLPs), which seeks generalized instance-level
predictors merely based on bag-level label proportions, has attracted widespread interest …
predictors merely based on bag-level label proportions, has attracted widespread interest …
Generalization performance of pure accuracy and its application in selective ensemble learning
J Wang, Y Qian, F Li, J Liang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The pure accuracy measure is used to eliminate random consistency from the accuracy
measure. Biases to both majority and minority classes in the pure accuracy are lower than …
measure. Biases to both majority and minority classes in the pure accuracy are lower than …
PAC learning linear thresholds from label proportions
A Brahmbhatt, R Saket… - Advances in Neural …, 2024 - proceedings.neurips.cc
Learning from label proportions (LLP) is a generalization of supervised learning in which the
training data is available as sets or bags of feature-vectors (instances) along with the …
training data is available as sets or bags of feature-vectors (instances) along with the …
Exploiting negative evidence for deep latent structured models
The abundance of image-level labels and the lack of large scale detailed annotations (eg
bounding boxes, segmentation masks) promotes the development of weakly supervised …
bounding boxes, segmentation masks) promotes the development of weakly supervised …