For learning purposes, representations of real world objects can be built by using the concept of dissimilarity (distance). In such a case, an object is characterized in a relative...
Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many machine learning problems [4, 16]. However, the exten...
One of the main factors affecting the effectiveness of ECOC methods for classification is the dependence among the errors of the computed codeword bits. We present an extensive ...
In this paper, the error-reject trade-off of linearly combined multiple classifiers is analysed in the framework of the minimum risk theory. Theoretical analysis described in [12,1...
Abstract. Given an arbitrary data set, to which no particular parametrical, statistical or geometrical structure can be assumed, different clustering algorithms will in general pr...
Genetic programming (GP) can automatically fuse given classifiers of diverse types to produce a combined classifier whose Receiver Operating Characteristics (ROC) are better than...
We provide several enhancements to our previously introduced algorithm for a sequential construction of a hybrid network of radial and perceptron hidden units [6]. At each stage, ...