Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
GTM: the generative topographic mapping
Neural Computation
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Self-Organizing Maps
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
NML computation algorithms for tree-structured multinomial Bayesian networks
EURASIP Journal on Bioinformatics and Systems Biology
A fast normalized maximum likelihood algorithm for multinomial data
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Relevance based visualization of large cancer patient populations
Proceedings of the 1st ACM International Health Informatics Symposium
CBTV: visualising case bases for similarity measure design and selection
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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When high-dimensional data vectors are visualized on a two- or three-dimensional display, the goal is that two vectors close to each other in the multi-dimensional space should also be close to each other in the low-dimensional space. Traditionally, closeness is defined in terms of some standard geometric distance measure, such as the Euclidean distance, based on a more or less straightforward comparison between the contents of the data vectors. However, such distances do not generally reflect properly the properties of complex problem domains, where changing one bit in a vector may completely change the relevance of the vector. What is more, in real-world situations the similarity of two vectors is not a universal property: even if two vectors can be regarded as similar from one point of view, from another point of view they may appear quite dissimilar. In order to capture these requirements for building a pragmatic and flexible similarity measure, we propose a data visualization scheme where the similarity of two vectors is determined indirectly by using a formal model of the problem domain; in our case, a Bayesian network model. In this scheme, two vectors are considered similar if they lead to similar predictions, when given as input to a Bayesian network model. The scheme is supervised in the sense that different perspectives can be taken into account by using different predictive distributions, i.e., by changing what is to be predicted. In addition, the modeling framework can also be used for validating the rationality of the resulting visualization. This model-based visualization scheme has been implemented and tested on real-world domains with encouraging results.