Machine Learning
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Recognition of dependent objects based on acyclic Markov models
Pattern Recognition and Image Analysis
| Hi-index | 0.00 |
The classical methods of pattern recognition theory presuppose the independence of elements of a recognizable set. Therefore, even in the case of interrelated objects, the decision on the class of each object is taken independent of the decision on the classes of other objects. At the same time, it is obvious that in this case it is necessary to make consistent decisions on the classes of elements of the interrelated array. In particular, this necessity stems from the prior supposition that neighboring objects of the interrelated array more often belong to one class than to different classes. In many practical problems, such a prior supposition proves to be very natural, enabling one to develop effective recognition algorithms.