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pro vyhledávání: '"Root, Jonathan"'
Autor:
Root, Jonathan
Let $f(x) \in \mathbb{F}_p[x]$, and define the orbit of $x\in \mathbb{F}_p$ under the iteration of $f$ to be the set \[ \mathcal{O}(x):=\{x,f(x),(f\circ f)(x),(f\circ f\circ f)(x),\dots\}. \] An orbit is a $k$-cycle if it is periodic of length $k$. I
Externí odkaz:
http://arxiv.org/abs/2410.00716
Autor:
Root, Jonathan, Kon, Mark
A metric probability space $(\Omega,d)$ obeys the ${\it concentration\; of\; measure\; phenomenon}$ if subsets of measure $1/2$ enlarge to subsets of measure close to 1 as a transition parameter $\epsilon$ approaches a limit. In this paper we conside
Externí odkaz:
http://arxiv.org/abs/2408.02540
Autor:
Root, Jonathan, Kon, Mark
We consider the sets of negatively associated (NA) and negatively correlated (NC) distributions as subsets of the space $\mathcal{M}$ of all probability distributions on $\mathbb{R}^n$, in terms of their relative topological structures within the top
Externí odkaz:
http://arxiv.org/abs/2304.09737
Autor:
Root, Jonathan
In this thesis we consider concentration inequalities and the concentration of measure phenomenon from a variety of angles. Sharp tail bounds on the deviation of Lipschitz functions of independent random variables about their mean are well known. We
Externí odkaz:
https://hdl.handle.net/2144/19741
We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on $n$-point nominal training data. We then train limited complexity models to imitate these scores based on
Externí odkaz:
http://arxiv.org/abs/1601.06105
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average $K$-NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-mar
Externí odkaz:
http://arxiv.org/abs/1502.01783
Autor:
Root, Jonathan B.
Thesis (M. A.)--Kansas State University, 2009.
Title from electronic thesis title page. Includes bibliographical references.
Title from electronic thesis title page. Includes bibliographical references.
Externí odkaz:
http://hdl.handle.net/2097/1371
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM us
Externí odkaz:
http://arxiv.org/abs/1405.0530
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