2018 - Fellow of the Indian National Academy of Engineering (INAE)
2012 - Fellow of the American Association for the Advancement of Science (AAAS)
2003 - IEEE Fellow For contributions to the theory and practice of fuzzy pattern recognition.
1985 - Fellow of Alfred P. Sloan Foundation
1933 - Fellow of the American Association for the Advancement of Science (AAAS)
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.
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.
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.
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
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SMOTE: synthetic minority over-sampling technique
Nitesh V. Chawla;Kevin W. Bowyer;Lawrence O. Hall;W. Philip Kegelmeyer.
Journal of Artificial Intelligence Research (2002)
SMOTE: Synthetic Minority Over-sampling Technique
N. V. Chawla;K. W. Bowyer;L. O. Hall;W. P. Kegelmeyer.
arXiv: Artificial Intelligence (2011)
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
Nitesh V. Chawla;Aleksandar Lazarevic;Lawrence O. Hall;Kevin W. Bowyer.
european conference on principles of data mining and knowledge discovery (2003)
Radiomics: the process and the challenges
Virendra Kumar;Yuhua Gu;Satrajit Basu;Anders Berglund.
Magnetic Resonance Imaging (2012)
Review of MR image segmentation techniques using pattern recognition.
J. C. Bezdek;L. O. Hall;L. P. Clarke.
Medical Physics (1993)
MRI segmentation: Methods and applications
L.P. Clarke;R.P. Velthuizen;M.A. Camacho;J.J. Heine.
Magnetic Resonance Imaging (1995)
A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain
L.O. Hall;A.M. Bensaid;L.P. Clarke;R.P. Velthuizen.
IEEE Transactions on Neural Networks (1992)
Automatic tumor segmentation using knowledge-based techniques
M.C. Clark;L.O. Hall;D.B. Goldgof;R. Velthuizen.
IEEE Transactions on Medical Imaging (1998)
Clustering with a genetically optimized approach
L.O. Hall;I.B. Ozyurt;J.C. Bezdek.
IEEE Transactions on Evolutionary Computation (1999)
Validity-guided (re)clustering with applications to image segmentation
A.M. Bensaid;L.O. Hall;J.C. Bezdek;L.P. Clarke.
IEEE Transactions on Fuzzy Systems (1996)
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