Add to ORKGIn this paper we present a new approach to data analysis based on flow distribution study in a flow network. Branches of the flow graph are interpreted as decision rules, whereas the flow graph is supposed to describe a decision algorithm. We propose to model decision processes as flow graphs and analyze decisions in terms of flow spreading in the graph
A flow network is a directed graph in which each edge has a capacity, bounding the amount of flow th...
Probabilistic decision graphs (PDGs) are a representation language for probability distributions bas...
Abstract. Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reas...
Abstract. In this paper we introduce a new kind of flow networks, called flow graphs, different to t...
This paper concerns some relationship between Bayes’ theorem and rough sets. It is revealed that any...
AbstractIn 1913 Jan Łukasiewicz proposed to use logic as mathematical foundations of probability. He...
Abstract. We consider association of decision trees and flow graphs, resulting in a new method of de...
This paper presents a collection of basics and application of Network flows in Graph theory which is...
The occurrence of diffusion on a graph is a prevalent and significant phenomenon, as evidenced by th...
In Bayesian structure learning, we are interested in inferring a distribution over the directed acyc...
AbstractThis paper proposes an approach to analysis of fuzzy inference systems. To this end, a flow ...
INTRODUCTION This chapter surveys the development of graphical models known as Bayesian networks, s...
Suppose we wish to build a model of data from a finite sequence of ordered observations, {Y1, Y2,......
AbstractProbabilistic decision graphs (PDGs) are a representation language for probability distribut...
Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represen...
A flow network is a directed graph in which each edge has a capacity, bounding the amount of flow th...
Probabilistic decision graphs (PDGs) are a representation language for probability distributions bas...
Abstract. Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reas...
Abstract. In this paper we introduce a new kind of flow networks, called flow graphs, different to t...
This paper concerns some relationship between Bayes’ theorem and rough sets. It is revealed that any...
AbstractIn 1913 Jan Łukasiewicz proposed to use logic as mathematical foundations of probability. He...
Abstract. We consider association of decision trees and flow graphs, resulting in a new method of de...
This paper presents a collection of basics and application of Network flows in Graph theory which is...
The occurrence of diffusion on a graph is a prevalent and significant phenomenon, as evidenced by th...
In Bayesian structure learning, we are interested in inferring a distribution over the directed acyc...
AbstractThis paper proposes an approach to analysis of fuzzy inference systems. To this end, a flow ...
INTRODUCTION This chapter surveys the development of graphical models known as Bayesian networks, s...
Suppose we wish to build a model of data from a finite sequence of ordered observations, {Y1, Y2,......
AbstractProbabilistic decision graphs (PDGs) are a representation language for probability distribut...
Probabilistic graphical models (PGMs) use graphs, either undirected, directed, or mixed, to represen...
A flow network is a directed graph in which each edge has a capacity, bounding the amount of flow th...
Probabilistic decision graphs (PDGs) are a representation language for probability distributions bas...
Abstract. Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reas...