M
Mariette Awad
Researcher at American University of Beirut
Publications - 136
Citations - 2903
Mariette Awad is an academic researcher from American University of Beirut. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 16, co-authored 119 publications receiving 1938 citations. Previous affiliations of Mariette Awad include IBM & GlobalFoundries.
Papers
More filters
Journal ArticleDOI
Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications
Ali Yassin,Youssef Nasser,Mariette Awad,Ahmed Al-Dubai,Ran Liu,Chau Yuen,Ronald Raulefs,Elias Aboutanios +7 more
TL;DR: This survey surveys different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts and discusses different localization-based applications, where the location information is critical to estimate.
Book ChapterDOI
Support Vector Regression
Mariette Awad,Rahul Khanna +1 more
TL;DR: The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems, and is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of support vectors.
Book ChapterDOI
Support Vector Machines for Classification
Mariette Awad,Rahul Khanna +1 more
TL;DR: This chapter focuses on SVM for supervised classification tasks only, providing SVM formulations for when the input space is linearly separable or linearly nonseparable and when the data are unbalanced, along with examples.
Book
Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
Mariette Awad,Rahul Khanna +1 more
TL;DR: Efficient Learning Machines as mentioned in this paper explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networks, kernel methods, and biologically-inspired techniques.
Journal ArticleDOI
Cooperative Heterogeneous Multi-Robot Systems: A Survey
TL;DR: More autonomous end-to-end solutions need to be experimentally tested and developed while incorporating natural language ontology and dictionaries to automate complex task decomposition and leveraging big data advancements to improve perception algorithms for robotics.