What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Pattern recognition, Segmentation, Cluster analysis and Image processing.
Lawrence O. Hall has researched Artificial intelligence in several fields, including Machine learning, Data mining and Computer vision.
Lawrence O. Hall combines subjects such as Classifier, Intrusion detection system and Set with his study of Machine learning.
His Pattern recognition course of study focuses on Receiver operating characteristic and Classifier.
His Cluster analysis research is multidisciplinary, incorporating elements of Fuzzy number, Initialization, Fuzzy logic and Pattern recognition.
His Image processing study combines topics in areas such as Scale-space segmentation and Segmentation-based object categorization.
His most cited work include:
- SMOTE: synthetic minority over-sampling technique (9316 citations)
- SMOTE: Synthetic Minority Over-sampling Technique (4150 citations)
- SMOTEBoost: Improving Prediction of the Minority Class in Boosting (974 citations)
What are the main themes of his work throughout his whole career to date?
Lawrence O. Hall mainly focuses on Artificial intelligence, Pattern recognition, Data mining, Machine learning and Fuzzy logic.
Lawrence O. Hall focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Computer vision and, in some cases, Volume.
His work carried out in the field of Pattern recognition brings together such families of science as Deep learning and Feature.
His work in Data mining covers topics such as Ensemble learning which are related to areas like Random forest.
His study in Training set extends to Machine learning with its themes.
His Fuzzy logic study incorporates themes from Algorithm, Set and Expert system.
He most often published in these fields:
- Artificial intelligence (65.96%)
- Pattern recognition (32.23%)
- Data mining (22.59%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (65.96%)
- Pattern recognition (32.23%)
- Segmentation (17.17%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Segmentation, Radiology and Convolutional neural network.
His work deals with themes such as Machine learning and Computer vision, which intersect with Artificial intelligence.
His research integrates issues of Histogram and Image in his study of Pattern recognition.
The concepts of his Segmentation study are interwoven with issues in Stereology, Concordance correlation coefficient, Depth of field and Reproducibility.
When carried out as part of a general Radiology research project, his work on Magnetic resonance imaging and Biopsy is frequently linked to work in Quantitative imaging, therefore connecting diverse disciplines of study.
His Classifier study incorporates themes from Random forest and Decision tree learning.
Between 2013 and 2021, his most popular works were:
- Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. (185 citations)
- Test–Retest Reproducibility Analysis of Lung CT Image Features (133 citations)
- Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches (132 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Pattern recognition, Feature, Segmentation and Convolutional neural network.
He has included themes like Machine learning and Computer vision in his Artificial intelligence study.
Lawrence O. Hall interconnects Region of interest, Cancer, Sample and Speedup in the investigation of issues within Pattern recognition.
The Feature study combines topics in areas such as Magnetic resonance imaging, Histogram and Radiomics.
His Segmentation study combines topics in areas such as Stereology, Concordance correlation coefficient, Depth of field and Reproducibility.
His study on Convolutional neural network also encompasses disciplines like
- Artificial neural network which is related to area like Classifier and Decision tree learning,
- Deep learning, which have a strong connection to Test set.
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