Abstract. In this paper we introduce a new kind of flow networks, called flow graphs, different to that proposed by Ford and Fulkerson. Flow graphs are meant to be used as a mathematical tool to analysis of information flow in decision al-gorithms, in contrast to material flow optimization considered in classical flow network analysis. In the proposed approach branches of the flow graph are in-terpreted as decision rules, while the whole flow graph can be understood as a representation of decision algorithm. The information flow in flow graphs is governed by Bayes ’ rule, however, in our case, the rule does not have probabil-istic meaning and is entirely deterministic. It describes simply information flow distribution in flow graphs. This p...
The objective of this research is to investigate the theoretical and computational aspects of the fl...
Abstract. Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reas...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
Abstract. We consider association of decision trees and flow graphs, resulting in a new method of de...
AbstractIn 1913 Jan Łukasiewicz proposed to use logic as mathematical foundations of probability. He...
In this paper we present a new approach to data analysis based on flow distribution study in a flow ...
This paper concerns some relationship between Bayes’ theorem and rough sets. It is revealed that any...
The application of flow networks on the example of the types of works distribution as well as the di...
This paper presents a collection of basics and application of Network flows in Graph theory which is...
Many large-scale and safety critical systems can be modeled as flow networks. Traditional approaches...
The behavior of complex systems is determined not only by the topological organization of their inte...
The use of flow-graph techniques for the analysis of complex feedback systems like those encountered...
This note gives a very brief introduction to the theory of network flows and some related topics in ...
The paper presents research results referring to the use of flow networks and ant colony algorithm i...
A flow network is a directed graph in which each edge has a capacity, bounding the amount of flow th...
The objective of this research is to investigate the theoretical and computational aspects of the fl...
Abstract. Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reas...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
Abstract. We consider association of decision trees and flow graphs, resulting in a new method of de...
AbstractIn 1913 Jan Łukasiewicz proposed to use logic as mathematical foundations of probability. He...
In this paper we present a new approach to data analysis based on flow distribution study in a flow ...
This paper concerns some relationship between Bayes’ theorem and rough sets. It is revealed that any...
The application of flow networks on the example of the types of works distribution as well as the di...
This paper presents a collection of basics and application of Network flows in Graph theory which is...
Many large-scale and safety critical systems can be modeled as flow networks. Traditional approaches...
The behavior of complex systems is determined not only by the topological organization of their inte...
The use of flow-graph techniques for the analysis of complex feedback systems like those encountered...
This note gives a very brief introduction to the theory of network flows and some related topics in ...
The paper presents research results referring to the use of flow networks and ant colony algorithm i...
A flow network is a directed graph in which each edge has a capacity, bounding the amount of flow th...
The objective of this research is to investigate the theoretical and computational aspects of the fl...
Abstract. Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reas...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...