Add to ORKG... theoretic classification (ITC) is introduced. Its principle relies on the likelihood of a data sample of transmitting its class label to data points in its vicinity. ITC's learning rule is linked to the concept of information potential and the approach is validated on Ripley's data set. We show that ITC may outperform classical classification algorithms, such as probabilistic neural networks and support vector machines
The authors have constructed an iterative, probabilistic reasoning architecture for classification p...
We discuss an information theoretic approach for categorizing and modeling dynamic processes. The a...
Information field theory (IFT), the information theory for fields, is a mathematical framework for s...
In this paper, supervised nonparametric information theoretic classification (ITC) is introduced. It...
Abstract. In this article we extend the (recently published) unsupervised information theoretic vect...
Villmann T, Hammer B, Schleif F-M, Geweniger T, Fischer T, Cottrell M. Prototype based classificatio...
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitione...
In pattern recognition, a classifier is trained solve the multiple hypotheses test-ing problem in wh...
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitione...
In supervised classification, one attempts to learn a model of how objects map to labels by selectin...
A classification architecture that uses probabilistic representation of support and conditionalizati...
Classification is a fundamental topic in the literature of data mining and all recent hot topics lik...
Classification is the allocation of an object to an existing category among several based on uncerta...
This dissertation illustrates how certain information-theoretic ideas and views on learning problems...
This paper contributes a tutorial level discussion of some interesting properties of the recent info...
The authors have constructed an iterative, probabilistic reasoning architecture for classification p...
We discuss an information theoretic approach for categorizing and modeling dynamic processes. The a...
Information field theory (IFT), the information theory for fields, is a mathematical framework for s...
In this paper, supervised nonparametric information theoretic classification (ITC) is introduced. It...
Abstract. In this article we extend the (recently published) unsupervised information theoretic vect...
Villmann T, Hammer B, Schleif F-M, Geweniger T, Fischer T, Cottrell M. Prototype based classificatio...
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitione...
In pattern recognition, a classifier is trained solve the multiple hypotheses test-ing problem in wh...
Outcome labeling ambiguity and subjectivity are ubiquitous in real-world datasets. While practitione...
In supervised classification, one attempts to learn a model of how objects map to labels by selectin...
A classification architecture that uses probabilistic representation of support and conditionalizati...
Classification is a fundamental topic in the literature of data mining and all recent hot topics lik...
Classification is the allocation of an object to an existing category among several based on uncerta...
This dissertation illustrates how certain information-theoretic ideas and views on learning problems...
This paper contributes a tutorial level discussion of some interesting properties of the recent info...
The authors have constructed an iterative, probabilistic reasoning architecture for classification p...
We discuss an information theoretic approach for categorizing and modeling dynamic processes. The a...
Information field theory (IFT), the information theory for fields, is a mathematical framework for s...