Corrigendum: Applications and techniques for fast machine learning in science.
Allison McCarn DeianaNhan TranJoshua AgarMichaela BlottGiuseppe Di GuglielmoJavier M. DuartePhilip C. HarrisScott HauckMia LiuMark S. NeubauerJennifer NgadiubaSeda Ogrenci-MemikMaurizio PieriniThea AarrestadSteffen BährJürgen BeckerAnne-Sophie BertholdRichard J. BonventreTomás E. Müller-BravoMarkus DiefenthalerZhen DongNick FritzscheAmir GholamiEkaterina GovorkovaDongning GuoKyle J. HazelwoodChristian HerwigBabar KhanSehoon KimThomas KlijnsmaYaling LiuKin Ho LoTri NguyenGianantonio PezzulloSeyedramin RasoulinezhadRyan A. RiveraKate ScholbergJustin SeligSougata SenDmitri StrukovWilliam TangSavannah ThaisKai Lukas UngerRicardo VilaltaBelinavon KrosigkShen WangThomas K. WarburtonPublished in: Frontiers Big Data (2024)
Keyphrases
- machine learning
- data mining
- computer science
- artificial intelligence
- machine learning methods
- computational intelligence
- machine learning algorithms
- knowledge acquisition
- knowledge discovery
- learning systems
- pattern recognition
- middle school
- explanation based learning
- inductive learning
- learning algorithm
- natural language processing
- knowledge representation
- active learning
- support vector
- decision trees
- feature selection
- machine learning approaches
- interdisciplinary field
- knowledge engineering
- supervised machine learning
- longitudinal study
- database
- learning problems
- learning tasks
- text mining
- support vector machine
- data analysis
- natural language
- website
- image processing
- computer vision