Neural networks for pattern recognition
Neural networks for pattern recognition
Texture classification using wavelet transform
Pattern Recognition Letters
Information theory and statistics: a tutorial
Communications and Information Theory
Estimating class priors in domain adaptation for word sense disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Non-stationary data sequence classification using online class priors estimation
Pattern Recognition
Quantifying counts and costs via classification
Data Mining and Knowledge Discovery
A framework for monitoring classifiers’ performance: when and why failure occurs?
Knowledge and Information Systems
Quantifying the proportion of damaged sperm cells based on image analysis and neural networks
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Quantification and semi-supervised classification methods for handling changes in class distribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
Vitality assessment of boar sperm using an adaptive LBP based on oriented deviation
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
| Hi-index | 0.00 |
Advances in image analysis make possible the automatic semen analysis in the veterinary practice. The proportion of sperm cells with damaged/intact acrosome, a major aspect in this assessment, depends strongly on several factors, including animal diversity and manipulation/ conservation conditions. For this reason, the class proportions have to be quantified for every future (test) semen sample. In this work, we evaluate quantification approaches based on the confusion matrix, the posterior probability estimates and a novel proposal based on the Hellinger distance. Our information theoretic-based approach to estimate the class proportions measures the similarity between several artificially generated calibration distributions and the test one at different stages: the data distributions and the classifier output distributions. Experimental results show that quantification can be conducted with a Mean Absolute Error below 0.02, what seems promising in this field.