What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Min-Ling Zhang mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Instance-based learning and Multi-label classification.
His Artificial intelligence research focuses on Training set, Cluster analysis and Feature vector.
His study in Multi label learning, Stability and Supervised learning are all subfields of Machine learning.
His Supervised learning study combines topics in areas such as Feature learning and Algorithmic learning theory.
His studies deal with areas such as Artificial neural network and Competitive learning as well as Instance-based learning.
His work on Classifier chains as part of general Multi-label classification study is frequently connected to Set, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His most cited work include:
- ML-KNN: A lazy learning approach to multi-label learning (1869 citations)
- A Review On Multi-Label Learning Algorithms (1461 citations)
- Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization (823 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Machine learning, Pattern recognition, Multi label learning and Feature vector.
His Training set and Instance-based learning study in the realm of Artificial intelligence interacts with subjects such as Set, Space and Generalization.
His Semi-supervised learning, Supervised learning, Ensemble learning and Feature learning study, which is part of a larger body of work in Machine learning, is frequently linked to Set, bridging the gap between disciplines.
His Pattern recognition study incorporates themes from Feature and Feature.
In Feature vector, Min-Ling Zhang works on issues like Cluster analysis, which are connected to Lift.
His work is dedicated to discovering how Multi-label classification, Algorithm are connected with Algorithmic learning theory and other disciplines.
He most often published in these fields:
- Artificial intelligence (81.61%)
- Machine learning (45.98%)
- Pattern recognition (36.78%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (81.61%)
- Pattern recognition (36.78%)
- Multi label learning (24.14%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Multi label learning, Machine learning and Feature vector.
His work on Class, Class imbalance and Training set as part of general Artificial intelligence study is frequently linked to Generalization and Space, bridging the gap between disciplines.
His Pattern recognition research integrates issues from Regularization, Process, Feature and Deep neural networks.
Min-Ling Zhang works in the field of Machine learning, namely Margin maximization.
His studies in Feature vector integrate themes in fields like Representation and Multi-label classification.
His study in Multi-label classification is interdisciplinary in nature, drawing from both Feature, Artificial neural network, Autoencoder, Softmax function and Representation.
Between 2017 and 2021, his most popular works were:
- Binary relevance for multi-label learning: an overview (105 citations)
- Feature-Induced Labeling Information Enrichment for Multi-Label Learning. (29 citations)
- Towards Enabling Binary Decomposition for Partial Label Learning (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Min-Ling Zhang spends much of his time researching Artificial intelligence, Multi label learning, Pattern recognition, Extraction and Specific-information.
His research brings together the fields of Machine learning and Artificial intelligence.
His Ranking study in the realm of Machine learning connects with subjects such as Set, Maximum a posteriori estimation and Label propagation.
His work in Pattern recognition covers topics such as Feature which are related to areas like Discriminative model, Class and k-nearest neighbors algorithm.
Min-Ling Zhang has researched Classifier in several fields, including Classifier and Outlier.
His Class imbalance research includes elements of State, Relevance and Natural language processing.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
