However, the activation of each neuron depends on the euclidean distance between a pattern and the neuron center. Therefore, the activation function is symmetrical and all attributes are considered equally relevant. This could be solved by altering the metric used in the activation function (i.e....
J VALLS, J. M.-ALER, R.-FERNANDEZ, 0.: Using a Mahalanobis-Like Distance to Train Radial Basis Neural Networks. IWANN 2005, Lecture Notes in Computer Scien- ce 3512, Proceedings of the 8th International vVork-Conference on Artificial Neural Networks IWANN 2005, pp. 257-263, 2005....
However, the activation of each neuron depends on the euclidean distance between a pattern and the neuron center. Therefore, the activation function is symmetrical and all attributes are considered equally relevant. This could be solved by altering the metric used in the activation function (i.e....
For many applications, such vector representation is not available and we only possess proximity data (distance, dissimilarity, similarity, ranks, etc.). In this paper, we consider a particular point of view on discriminant analysis from dissimilarity data. Our approach is inspired by the Gaussian...
This paper introduces a new model-fitting based method for indoor scans, relying on Mahalanobis distance (MD) and histogram-driven kd-like point cloud division. The hierarchical, well-balanced point cloud subdivision process enables a shallow yet sufficient partition, preventing the adjacent planar poi...
This paper introduces a new model-fitting based method for indoor scans, relying on Mahalanobis distance (MD) and histogram-driven kd-like point cloud division. The hierarchical, well-balanced point cloud subdivision process enables a shallow yet sufficient partition, preventing the adjacent planar poi...