What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
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.
His most cited work include:
- A Dataset for Breast Cancer Histopathological Image Classification (379 citations)
- Breast cancer histopathological image classification using Convolutional Neural Networks (332 citations)
- Automatic recognition of handwritten numerical strings: a recognition and verification strategy (205 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (82.65%)
- Pattern recognition (52.51%)
- Machine learning (32.42%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (82.65%)
- Pattern recognition (52.51%)
- Machine learning (32.42%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- Multiple instance learning for histopathological breast cancer image classification (102 citations)
- Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses (82 citations)
- Double Transfer Learning for Breast Cancer Histopathologic Image Classification (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
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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