Communications of the ACM - Special issue on parallelism
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Acquisition of dynamic control knowledge for a robotic manipulator
Proceedings of the seventh international conference (1990) on Machine learning
Case-based reasoning
Artificial Intelligence Review - Special issue on lazy learning
Lazy learning
Machine Learning - Special issue on learning with probabilistic representations
Self-Organizing Maps
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
On predictive distributions and Bayesian networks
Statistics and Computing
Bayes Optimal Instance-Based Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
A study of instance-based algorithms for supervised learning tasks: mathematical, empirical, and psychological evaluations
On supervised selection of Bayesian networks
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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We introduce a distance measure based on the idea that two vectors are considered similar if they lead to similar predictive probability distributions. The suggested approach avoids the scaling problem inherent to many alternative techniques as the method automatically transforms the original attribute space to a probability space where all the numbers lie between 0 and 1. The method is also flexible in the sense that it allows different attribute types (discrete or continuous) in the same consistent framework. To study the validity of the suggested measure, we ran a series of experiments with publicly available data sets. The empirical results demonstrate that the unsupervised distance measure is sensible in the sense that it can be used for discovering the hidden clustering structure of the data.