no code implementations • 27 Jun 2023 • Nader H. Bshouty, Catherine A. Haddad-Zaknoon
We develop upper and lower bounds on the number of tests required to detect $\ell$ defective items in both the adaptive and non-adaptive settings while considering scenarios where no prior knowledge of $d$ is available, and situations where an estimate of $d$ or at least some non-trivial upper bound on $d$ is available.
no code implementations • 10 Aug 2021 • 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 $n$.
no code implementations • 5 Nov 2019 • Nader H. Bshouty, George Haddad, Catherine A. Haddad-Zaknoon
In this paper, we study the measures $$c_{\cal M}(d)=\lim_{n\to \infty} \frac{m_{\cal M}(n, d)}{\ln n} \mbox{ and } c_{\cal M}=\lim_{d\to \infty} \frac{c_{\cal M}(d)}{d}.$$ In the literature, the analyses of such models only give upper bounds for $c_{\cal M}(d)$ and $c_{\cal M}$, and for some of them, the bounds are not tight.
no code implementations • 23 Jan 2019 • Nader H. Bshouty, Catherine A. Haddad-Zaknoon
In this paper we study the adaptive learnability of decision trees of depth at most $d$ from membership queries.
no code implementations • 4 Jul 2017 • Nader H. Bshouty, Catherine A. Haddad-Zaknoon
Moreover, we construct a cosine bound from which we build the Maximum Cosine Perceptron algorithm or, for short, the MCP algorithm.
