3 citations · 3 across the 6 of their papers we have counts for
8 papers
On Detecting Some Defective Items in Group Testing
Nader H. Bshouty, Catherine A. Haddad-Zaknoon
Group testing is an approach aimed at identifying up to defective items among a total of elements. This is accomplished by examining subsets to determine if at least one de…
Heuristic Random Designs for Exact Identification of Defectives Using Single Round Non-adaptive Group Testing and Compressed Sensing
Catherine A. Haddad-Zaaknoon
Among the challenges that the COVID-19 pandemic outbreak revealed is the problem to reduce the number of tests required for identifying the virus carriers in order to contain the v…
On Learning and Testing Decision Tree
Nader H. Bshouty, Catherine A. Haddad-Zaknoon
In this paper, we study learning and testing decision tree of size and depth that are significantly smaller than the number of attributes . Our main result addresses the problem…
Optimal Deterministic Group Testing Algorithms to Estimate the Number of Defectives
Nader H. Bshouty, Catherine A. Haddad-Zaknoon
We study the problem of estimating the number of defective items within a pile of elements up to a multiplicative factor of , using deterministic group testing algorit…
Optimal Randomized Group Testing Algorithm to Determine the Number of Defectives
Nader H. Bshouty, Catherine A. Haddad-Zaknoon, Raghd Boulos +4
We study the problem of determining exactly the number of defective items in an adaptive Group testing by using a minimum number of tests. We improve the existing algorithm and pro…
Bounds for the Number of Tests in Non-Adaptive Randomized Algorithms for Group Testing
Nader H. Bshouty, George Haddad, Catherine A. Haddad-Zaknoon
We study the group testing problem with non-adaptive randomized algorithms. Several models have been discussed in the literature to determine how to randomly choose the tests. For…
Adaptive Exact Learning of Decision Trees from Membership Queries
Nader H. Bshouty, Catherine A. Haddad-Zaknoon
In this paper we study the adaptive learnability of decision trees of depth at most from membership queries. This has many applications in automated scientific discovery such a…
The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
Nader H. Bshouty, Catherine A. Haddad-Zaknoon
In this work, we introduce a mathematical framework, called the Maximum Cosine Framework or MCF, for deriving new linear classifiers. The method is based on selecting an appropriat…