Unsupervised tissue type segmentation of 3D dual-echo MR head data
Image and Vision Computing - Special issue: information processing in medical imaging 1991
C4.5: programs for machine learning
C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Proceedings of the conference on Visualization '01
The Transfer Function Bake-Off
IEEE Computer Graphics and Applications
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
High-Quality Two-Level Volume Rendering of Segmented Data Sets on Consumer Graphics Hardware
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
A Novel Interface for Higher-Dimensional Classification of Volume Data
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A cluster-space visual interface for arbitrary dimensional classification of volume data
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
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This paper analyzes how to introduce machine learning algorithms into the process of direct volume rendering. A conceptual framework for the optical property function elicitation process is proposed and particularized for the use of attribute-value classifiers. The process is evaluated in terms of accuracy and speed using four different off-the-shelf classifiers (J48, Naïve Bayes, Simple Logistic and ECOC-Adaboost). The empirical results confirm the classification of biomedical datasets as a tough problem where an opportunity for further research emerges.