His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Feature extraction and Classifier. His work in Deep learning, Feature, Contextual image classification, Pattern recognition and Support vector machine are all subfields of Artificial intelligence research. His work in the fields of Pattern recognition, such as Segmentation and Feature matching, overlaps with other areas such as Signature and Polygon.
His Artificial neural network, Genetic algorithm and Feature selection study are his primary interests in Machine learning. His Feature extraction study which covers Field that intersects with Iris recognition and Feature learning. Luiz S. Oliveira combines subjects such as Data mining and Feature set with his study of Classifier.
Luiz S. Oliveira mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Classifier and Feature extraction. His Support vector machine, Segmentation, Convolutional neural network, Artificial neural network and Feature vector investigations are all subjects of Artificial intelligence research. His Pattern recognition study incorporates themes from Local binary patterns, Image and Feature.
His Machine learning course of study focuses on Pattern recognition and Identification. The concepts of his Classifier study are interwoven with issues in Pattern recognition problem and Biometrics. His work in Feature extraction addresses subjects such as Contextual image classification, which are connected to disciplines such as Transfer of learning.
Luiz S. Oliveira mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Classifier. His study in Feature extraction, Image, Segmentation, Representation and Convolutional neural network are all subfields of Artificial intelligence. His Convolutional neural network research is multidisciplinary, incorporating perspectives in Object detection, Task, Feature learning and Robustness.
His work deals with themes such as Contextual image classification, Identification, Local binary patterns and Transfer of learning, which intersect with Pattern recognition. His study looks at the relationship between Machine learning and fields such as Adversarial system, as well as how they intersect with chemical problems. His Classifier research is multidisciplinary, incorporating elements of Biometrics, Data stream mining, Resampling, Evolutionary algorithm and Discriminative model.
Artificial intelligence, Pattern recognition, Machine learning, Classifier and Feature extraction are his primary areas of study. His study in Deep learning, Adversarial system, Robustness, Image processing and Biometrics is carried out as part of his studies in Artificial intelligence. His Pattern recognition research is multidisciplinary, relying on both Contextual image classification and Image.
The various areas that he examines in his Machine learning study include Representation and Field. His Classifier research integrates issues from Resampling, Random forest, Discriminative model and Imbalanced data. He has researched Feature extraction in several fields, including Learning classifier system, Cognitive neuroscience of visual object recognition and Training set.
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A Dataset for Breast Cancer Histopathological Image Classification
Fabio A. Spanhol;Luiz S. Oliveira;Caroline Petitjean;Laurent Heutte.
IEEE Transactions on Biomedical Engineering (2016)
Breast cancer histopathological image classification using Convolutional Neural Networks
Fabio Alexandre Spanhol;Luiz S. Oliveira;Caroline Petitjean;Laurent Heutte.
international joint conference on neural network (2016)
Automatic recognition of handwritten numerical strings: a recognition and verification strategy
L.S. Oliveira;R. Sabourin;F. Bortolozzi;C.Y. Suen.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Dynamic selection of classifiers-A comprehensive review
Alceu S. Britto;Alceu S. Britto;Robert Sabourin;Luiz E. S. Oliveira.
Pattern Recognition (2014)
Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers
D. Bertolini;L. S. Oliveira;E. Justino;R. Sabourin.
Pattern Recognition (2010)
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Rayson Laroca;Evair Severo;Luiz A. Zanlorensi;Luiz S. Oliveira.
international joint conference on neural network (2018)
Learning features for offline handwritten signature verification using deep convolutional neural networks
Luiz G. Hafemann;Robert Sabourin;Luiz S. Oliveira.
Pattern Recognition (2017)
PKLot – A robust dataset for parking lot classification
Paulo R.L. de Almeida;Luiz S. Oliveira;Alceu S. Britto;Eunelson J. Silva.
Expert Systems With Applications (2015)
A METHODOLOGY FOR FEATURE SELECTION USING MULTIOBJECTIVE GENETIC ALGORITHMS FOR HANDWRITTEN DIGIT STRING RECOGNITION
Luiz E. Soares de Oliveira;Robert Sabourin;Flávio Bortolozzi;Ching Y. Suen.
International Journal of Pattern Recognition and Artificial Intelligence (2003)
Multiple instance learning for histopathological breast cancer image classification
P. J. Sudharshan;Caroline Petitjean;Fabio A. Spanhol;Luiz Eduardo Soares de Oliveira.
Expert Systems With Applications (2019)
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