Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
Learning from noisy labels with deep neural networks: A survey
Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …
amounts of big data. However, the quality of data labels is a concern because of the lack of …
Blind image quality assessment via vision-language correspondence: A multitask learning perspective
We aim at advancing blind image quality assessment (BIQA), which predicts the human
perception of image quality without any reference information. We develop a general and …
perception of image quality without any reference information. We develop a general and …
A review of methods for imbalanced multi-label classification
Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …
classification where each data instance is associated with several labels simultaneously …
Deep learning for extreme multi-label text classification
Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …
document its most relevant subset of class labels from an extremely large label collection …
Active learning query strategies for classification, regression, and clustering: A survey
Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …
SGM: sequence generation model for multi-label classification
Multi-label classification is an important yet challenging task in natural language processing.
It is more complex than single-label classification in that the labels tend to be correlated …
It is more complex than single-label classification in that the labels tend to be correlated …
Integrative few-shot learning for classification and segmentation
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that
aims to both classify and segment target objects in a query image when the target classes …
aims to both classify and segment target objects in a query image when the target classes …
MLACO: A multi-label feature selection algorithm based on ant colony optimization
M Paniri, MB Dowlatshahi… - Knowledge-Based Systems, 2020 - Elsevier
Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted
interest and increasingly applied to different fields. In such learning processes, unlike single …
interest and increasingly applied to different fields. In such learning processes, unlike single …
[图书][B] An introduction to machine learning
M Kubat - 2017 - Springer
Machine learning has come of age. And just in case you might think this is a mere platitude,
let me clarify. The dream that machines would one day be able to learn is as old as …
let me clarify. The dream that machines would one day be able to learn is as old as …