What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Artificial intelligence, Machine learning, Gesture recognition, Gesture and Speech recognition are his primary areas of study.
The study incorporates disciplines such as Pattern recognition and Natural language processing in addition to Artificial intelligence.
His biological study spans a wide range of topics, including Variety and Categorization.
His Gesture recognition research is under the purview of Computer vision.
His Gesture research focuses on Lexicon and how it relates to Temporal database, Human–computer interaction and Presentation.
His Model selection study incorporates themes from Particle swarm optimization, Domain knowledge and Selection.
His most cited work include:
- The segmented and annotated IAPR TC-12 benchmark (170 citations)
- Chalearn looking at people challenge 2014: Dataset and results (154 citations)
- Multi-modal gesture recognition challenge 2013: dataset and results (143 citations)
What are the main themes of his work throughout his whole career to date?
Hugo Jair Escalante mainly investigates Artificial intelligence, Machine learning, Data mining, Pattern recognition and Genetic programming.
His Artificial intelligence research includes elements of Computer vision and Natural language processing.
His study in State extends to Machine learning with its themes.
His study looks at the relationship between Data mining and topics such as Automatic image annotation, which overlap with Visual Word.
His Genetic programming research integrates issues from Deep learning, Feature learning and Feature vector.
The various areas that he examines in his Gesture study include Speech recognition and One-shot learning.
He most often published in these fields:
- Artificial intelligence (66.67%)
- Machine learning (38.10%)
- Data mining (14.76%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (66.67%)
- Machine learning (38.10%)
- Data science (7.62%)
In recent papers he was focusing on the following fields of study:
Hugo Jair Escalante spends much of his time researching Artificial intelligence, Machine learning, Data science, Field and Facial recognition system.
Hugo Jair Escalante combines subjects such as Pattern recognition and Natural language processing with his study of Artificial intelligence.
His study in the field of Text mining also crosses realms of Alphabetical order.
Hugo Jair Escalante has included themes like Representation, Feature extraction and State in his Machine learning study.
His Data science research incorporates elements of Textual information and Authentication.
His research in the fields of Anti spoofing overlaps with other disciplines such as Face.
Between 2017 and 2021, his most popular works were:
- Taking Human out of Learning Applications: A Survey on Automated Machine Learning (121 citations)
- Analysis of the AutoML Challenge Series 2015–2018 (39 citations)
- Overview of MEX-A3T at IberEval 2018: Authorship and Aggressiveness Analysis in Mexican Spanish Tweets. (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His scientific interests lie mostly in Artificial intelligence, Machine learning, Field, Big Five personality traits and Modalities.
His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition.
His Pattern recognition study combines topics in areas such as Watershed and Facial expression.
His Genetic programming, Feature learning and Deep learning study, which is part of a larger body of work in Machine learning, is frequently linked to Everyday life and Order, bridging the gap between disciplines.
His research integrates issues of Feature, Data science and Benchmark in his study of Facial recognition system.
His work carried out in the field of Feature vector brings together such families of science as Heuristics, Data mining and Support vector machine.
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