Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
An Application of Machine Learning Techniques for the Classification of Glaucomatous Progression
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
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The standardized visual field assessment, which measures visual function in 76 locations of the central visual area, is an important diagnostic tool in the treatment of the eye disease glaucoma. It helps determine whether the disease is stable or progressing towards blindness, with important implications for treatment. Automatic techniques to classify patients based on this assessment have had limited success, primarily due to the high variability of individual visual field measurements. The purpose of this paper is to describe the problem of visual field classification to the data mining community, and assess the success of data mining techniques on it. Preliminary results show that machine learning methods rival existing techniques for predicting whether glaucoma is progressing--though we have not yet been able to demonstrate improvements that are statistically significant. It is likely that further improvement is possible, and we encourage others to work on this important practical data mining problem.