What is he best known for?
The fields of study Yoav Freund is best known for:
- Machine learning
- Computational learning theory
- Statistics
His Machine learning study has been linked to subjects such as Boosting (machine learning) and Value (mathematics).
In the field of Mathematical analysis he connects related research areas like Generalization, Multiplicative function, Bounded function and Upper and lower bounds.
His Generalization study frequently links to adjacent areas such as Mathematical analysis.
While working in this field, he studies both Upper and lower bounds and Algorithm.
He performs multidisciplinary study in the fields of Algorithm and Mathematical optimization via his papers.
He integrates Mathematical optimization with Machine learning in his research.
Yoav Freund links relevant research areas such as Matching (statistics) and Value (mathematics) in the realm of Statistics.
In his works, Yoav Freund conducts interdisciplinary research on Matching (statistics) and Statistics.
Artificial intelligence and Learnability are commonly linked in his work.
His most cited work include:
- A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (13819 citations)
- The Nonstochastic Multiarmed Bandit Problem (1897 citations)
- Boosting a Weak Learning Algorithm by Majority (1411 citations)
What are the main themes of his work throughout his whole career to date
Yoav Freund regularly ties together related areas like Pattern recognition (psychology) in his Artificial intelligence studies.
Pattern recognition (psychology) and Artificial intelligence are commonly linked in his work.
By researching both Machine learning and Random forest, Yoav Freund produces research that crosses academic boundaries.
Yoav Freund undertakes interdisciplinary study in the fields of Random forest and Boosting (machine learning) through his research.
His work blends Boosting (machine learning) and AdaBoost studies together.
He performs multidisciplinary study in Algorithm and Programming language in his work.
While working in this field, he studies both Programming language and Algorithm.
His work on Mathematical analysis is being expanded to include thematically relevant topics such as Generalization.
Yoav Freund regularly ties together related areas like Mathematical analysis in his Generalization studies.
Yoav Freund most often published in these fields:
- Artificial intelligence (73.68%)
- Machine learning (44.74%)
- Boosting (machine learning) (34.21%)
What were the highlights of his more recent work (between 2010-2019)?
- Bounded function (50.00%)
- Algorithm (50.00%)
- Differential privacy (50.00%)
In recent works Yoav Freund was focusing on the following fields of study:
His Mathematical analysis study frequently involves adjacent topics like Bounded function, Distribution (mathematics) and Generalization.
In his articles, he combines various disciplines, including Distribution (mathematics) and Mathematical analysis.
His Algorithm study frequently draws parallels with other fields, such as Differential privacy.
Many of his studies involve connections with topics such as Algorithm and Differential privacy.
He incorporates Machine learning and Stability (learning theory) in his studies.
He integrates several fields in his works, including Stability (learning theory) and Machine learning.
His work in Human Protein Atlas is not limited to one particular discipline; it also encompasses Biochemistry.
Yoav Freund merges Biochemistry with Gene in his research.
Yoav Freund regularly ties together related areas like Protein expression in his Gene studies.
Between 2010 and 2019, his most popular works were:
- An active texture-based digital atlas enables automated mapping of structures and markers across brains (20 citations)
- Typical Stability (5 citations)
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