Eva Tardos: Algorithms, algorithmic game theory.
Two popular DR techniques are compared in relation to their ability to extract the most salient features, allow gene selection, and minimize the various interferences due to algorithmic approximation errors and variable noise covers.
In other words, in terms of algorithmic design, classical clustering algorithms use membership variables taking values of {0,1}, where fuzzy clustering uses continuous membership degree variables (μ i,k ) with a range of [0,1] where (sum _{i} mu _{i,k}=1).
Systems of polynomial equations in several variables from the algorithmic viewpoint.
An algorithmic description of the situational variables is formulated and the measurement data of the test drive is analyzed.
Although the comparisons are against the same benchmarking set, we are aware of several other confounding variables, such as algorithmic differences, species representation/distribution or genome annotation, that can all affect the results.
Topics include A/B testing, sequential decision making and bandits, decision theory, risk minimization and generalization, Markov decision processes, reinforcement learning, analysis of observational data, instrumental variable analysis, and algorithmic fairness of personalized decision policies.
The table above illustrates the variables found both by algorithmic selection and expert selection.
While decision-making algorithms are inherently not biased and algorithmic decision-making depends on a number of variables -- including how the software is designed, developed, deployed and the quality, integrity, and representativeness of the underlying data sources -- there is a need for a new approach to define and design decision-making algorithms.
For small to moderately sized linear systems (say, n ≤ 1,000), the favoured numerical method is Gaussian elimination and its variants; this is simply a precisely stated algorithmic variant of the method of elimination of variables that is introduced in elementary algebra.
Multivariate, algorithmic approaches allow optimization of combinations of clinical variables to predict outcome.
