Results 51 to 60 of about 86,158 (257)
Three Problems for Decision Rule Systems from Closed Classes
AxiomsThe study of the relationships between DRSs (Decision Rule Systems) and DTs (Decision Trees) is of considerable interest in computer science. In this paper, we consider classes of DRSs that are closed under specific operations.
Kerven Durdymyradov, Mikhail Moshkov
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Behavior Trees in Robotics and AI: An Introduction
, 2020 A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game.
Colledanchise, Michele, Ögren, Petter
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Machine Learning for Green Solvents: Assessment, Selection and Substitution
Advanced Science, EarlyView.Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta +4 more
wiley +1 more source
Rationality and dynamic consistency under risk and uncertainty [PDF]
, 2013 For choice with deterministic consequences, the standard rationality hypothesis is ordinality - i.e., maximization of a weak preference ordering. For choice under risk (resp.
Hammond, Peter J., Zank, Horst
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Advanced Science, EarlyView.ABSTRACT This special issue marking the University of Bath's 60th anniversary offers an opportunity to reflect on nearly a decade of research into the evolution of gene regulatory networks (GRNs) from members of the lab and elsewhere. Our goal is to understand how GRNs rewire and how new transcription factor (TF) functions evolve. Using an experimental
Tiffany B. Taylor, Alan M. Rice
wiley +1 more source
A System for Induction of Oblique Decision Trees [PDF]
, 1994 This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree.
Kasif, S., Murthy, S. K., Salzberg, S.
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Advanced Energy and Sustainability Research, EarlyView.This study proposes a method to increase the value of solar power in balancing markets by managing prediction errors. The approach models prediction uncertainties and quantifies reserve requirements based on a probabilistic model. This enables the more reliable participation of photovoltaic plants in balancing markets across multiple sites, especially ...
Jindan Cui +3 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
In the Maze of Data Languages [PDF]
, 2012 In data languages the positions of strings and trees carry a label from a finite alphabet and a data value from an infinite alphabet. Extensions of automata and logics over finite alphabets have been defined to recognize data languages, both in the ...
D'Antoni, Loris
core
Subexponential convergence for information aggregation on regular trees
, 2011 We consider the decentralized binary hypothesis testing problem on trees of bounded degree and increasing depth. For a regular tree of depth t and branching factor k>=2, we assume that the leaves have access to independent and identically distributed ...
Kanoria, Yashodhan, Montanari, Andrea
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