Soft sensor plays a progressively significant role in modern industrial processes. However,
process variables usually have complex distribution characteristics, which can adversely …

Multiple-instance learning (MIL) is a variation of supervised learning, where samples are
represented by labeled bags, each containing sets of instances. The individual labels of the …

Multi-instance learning (MIL) is a potent framework for solving weakly supervised problems,
with bags containing multiple instances. Various embedding methods convert each bag into …

Multi-instance learning (MIL) deals with the tasks where each example is represented by a
bag of instances. A bag is positive if it contains at least one positive instance, and negative …

The performance of the single kernel-based kernel density estimator (SK-KDE) in fitting a
unimodal probability density function (PDF) depends on the choice of kernel function and …

Class imbalance is a common problem in machine learning, particularly in classification
tasks. When the distribution of instances across known classes is biased or skewed, this …

Multi-instance learning (MIL) handles complex structured data represented by bags and
their instances. MIL embedded algorithms based on representative instance selection …

Abstract In Multiple Instance Learning (MIL) problem for sequence data, the instances inside
the bags are sequences. In some real world applications such as bioinformatics, comparing …

The objective of this proposal is to select and analyze, functionally and computationally, a
set of algorithms used for the detection of defects by automatic visual inspection, which …

Ionizing-radiation-resistant bacteria (IRRB) could be used for biore-mediation of radioactive
wastes and in the therapeutic industry. Limited computational works are available for the …