HICSS
2011
IEEE
13 years 2 months ago
2011
IEEE
We investigate the decision process as applied to the practical task of choosing a financial portfolio. We developed PortfolioCompare, an interactive visual analytic decision sup...
TSMC
2010
13 years 5 months ago
2010
Abstract--The aim of this paper is to present a decision support system model with two important characteristic: 1) mobile technologies are applied in the decision process and 2) t...
ICMLA
2010
13 years 9 months ago
2010
Abstract--We study how to effectively integrate reinforcement learning (RL) and programming languages via adaptation-based programming, where programs can include non-deterministic...
NECO
2008
13 years 11 months ago
2008
Previous theoretical work has shown that a single layer neural network can implement the optimal decision process for simple, two alternative forced choice (2AFC) tasks. However, ...
JAIR
2006
13 years 11 months ago
2006
A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decisiontheoretic...
ANOR
2010
13 years 11 months ago
2010
Abstract In the controlled ovary hyperstimulation (COH) cycle of the in vitro fertilization-embryo transfer (IVFET) therapy, the clinicians observe the patients' responses to ...
ICMAS
2000
14 years 8 days ago
2000
In this paper, we formulate agent's decision process under the framework of Markov decision processes, and in particular, the multi-agent extension to Markov decision process...
ECIS
2000
14 years 8 days ago
2000
Different models of Decision Support Systems (DSS) are used in medicine to help physicians in disease diagnosis, prognosis evaluation and therapy prescription. The DSS models rely ...
ASUNAM
2010
IEEE
14 years 14 days ago
2010
IEEE
This paper aims at studying the roles of social features (as obtained from social networking sources) in buyers' decision process when they are searching for products to buy. ...
ECML
2007
Springer
14 years 2 months ago
2007
Springer
Abstract. Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the deci...