AI
2006
Springer
13 years 8 months ago
2006
Springer
Gliomas are diffuse, invasive brain tumors. We propose a 3D classification-based diffusion model, cdm, that predicts how a glioma will grow at a voxel-level, on the basis of featur...
AI
2006
Springer
13 years 8 months ago
2006
Springer
AI
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value funct...
AI
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. Adaptive Benford's Law [1] is a digital analysis technique that specifies the probabilistic distribution of digits for many commonly occurring phenomena, even for in...
AI
2006
Springer
13 years 8 months ago
2006
Springer
Sentence selection shares some but not all the characteristics of Automatic Text Categorization. Therefore some but not all the same techniques should be used. In this paper we stu...
AI
2006
Springer
13 years 8 months ago
2006
Springer
We propose LAZY arc-reversal with variable elimination (LAZY-ARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZY-ARVE is an improvement upon LAZY ar...
AI
2006
Springer
13 years 8 months ago
2006
Springer
We focus on clustering gene expression temporal profiles, and propose a novel, simple algorithm that is powerful enough to find an efficient distribution of genes over clusters. We...
AI
2006
Springer
13 years 8 months ago
2006
Springer
In the context of probabilistic verification, we provide a new notion of trace-equivalence divergence between pairs of Labelled Markov processes. This divergence corresponds to the...
AI
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. We present a performance analysis of three linear dimensionality reduction techniques: Fisher's discriminant analysis (FDA), and two methods introduced recently base...
AI
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. A conditioning graph (CG) is a graphical structure that attempt to minimize the implementation overhead of computing probabilities in belief networks. A conditioning grap...