Lior Rokach mainly focuses on Artificial intelligence, Data mining, Machine learning, Recommender system and Knowledge extraction. As a part of the same scientific family, he mostly works in the field of Artificial intelligence, focusing on Pattern recognition and, on occasion, Subspace topology and Space decomposition. In general Data mining study, his work on Decision tree often relates to the realm of Decomposition, thereby connecting several areas of interest.
His research in Machine learning focuses on subjects like k-anonymity, which are connected to Data set, Information sensitivity and Data integrity. His Recommender system research incorporates elements of Context and Multimedia. His Knowledge extraction study frequently links to related topics such as Data science.
His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Recommender system and Decision tree. His Artificial intelligence research integrates issues from Natural language processing and Pattern recognition. The Machine learning study which covers Malware that intersects with Support vector machine.
Lior Rokach has researched Data mining in several fields, including Data science, Feature selection and Cluster analysis. His work focuses on many connections between Recommender system and other disciplines, such as Context, that overlap with his field of interest in Mobile device. His study in Decision tree concentrates on Decision tree learning and Alternating decision tree.
Lior Rokach mainly focuses on Artificial intelligence, Machine learning, Deep learning, Process and Malware. Lior Rokach interconnects Context and Pattern recognition in the investigation of issues within Artificial intelligence. His research related to Recommender system and Boosting might be considered part of Machine learning.
The various areas that Lior Rokach examines in his Recommender system study include Event and Database activity monitoring. The Artificial neural network study combines topics in areas such as Electronic engineering, Data mining and Distortion. His Data mining research is multidisciplinary, incorporating elements of Similarity and Encoding.
Lior Rokach mainly investigates Artificial intelligence, Machine learning, Malware, Classifier and Ensemble learning. His research brings together the fields of Pattern recognition and Artificial intelligence. His Machine learning study incorporates themes from Graph embedding, Mobile device and Measure.
His Malware research includes themes of Cloud computing and Support vector machine. Lior Rokach focuses mostly in the field of Classifier, narrowing it down to topics relating to Cluster analysis and, in certain cases, Mobile computing, Recommender system, Gradient boosting and Feature engineering. His Random forest study integrates concerns from other disciplines, such as Decision tree and Boosting.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Introduction to Recommender Systems Handbook
Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2011)
Recommender Systems Handbook
Francesco Ricci;Lior Rokach;Bracha Shapira;Paul B. Kantor.
Data Mining and Knowledge Discovery Handbook
Oded Maimon;Lior Rokach.
Data Mining with Decision Trees: Theory and Applications
Lior Rokach;Oded Maimon.
Artificial Intelligence Review (2010)
Recommender Systems: Introduction and Challenges
Francesco Ricci;Lior Rokach;Bracha Shapira.
Recommender Systems Handbook (2015)
Top-down induction of decision trees classifiers - a survey
L. Rokach;O. Maimon.
systems man and cybernetics (2005)
Data Mining and Knowledge Discovery Handbook, 2nd ed
Oded Z. Maimon;Lior Rokach.
Ensemble learning: A survey
Omer Sagi;Lior Rokach.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (2018)
Pattern Classification Using Ensemble Methods
Profile was last updated on December 6th, 2021.
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