Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
Elements of information theory
Elements of information theory
An entropy-based learning algorithm of Bayesian conditional trees
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Statistical color models with application to skin detection
International Journal of Computer Vision
Multivariate Information Bottleneck
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Learning with mixtures of trees
The Journal of Machine Learning Research
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Skin Detection: A Bayesian Network Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin detection using pairwise models
Image and Vision Computing
Tree-dependent component analysis
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Probability approximation using best-tree distribution for skin detection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Colour image segmentation in various illumination circumstances
CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control & signal processing
| Hi-index | 0.00 |
This paper presents a new approach for machine learning to deal with the problem of classification and/or probability approximation. Our contribution is based on the Optimal-Spanning-Tree distributions that are widely used in many optimization areas. The rationale behind this study is that in some cases the approximation of true class probability given by an Optimal-Spanning-Tree is not unique and might be chosen randomly. Furthermore, the user can specify the error tolerance between the tree weights that he/she can accept to manage the information of these kinds of trees. Therefore, the main idea of this work consists in focusing and highlighting the performance of each possible K(K@?N) Optimal-Spanning-Tree and making some assumptions, to propose the mixture of the K-Optimal-Spanning-Trees approximating the true class probability in a supervised algorithm. The theoretical proof of the K-Optimal-Spanning-Trees' mixture is given. Furthermore, the performance of our method is assessed for Skin/Non-Skin classification in the Compaq database by measuring the Receiver Operating Characteristic curve and its under area. These measures have proved better results of the proposed model compared with a random Optimal-Spanning-Tree model and the baseline one.