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For someone wishing to become an expert on machine learning, mastering a handful of baseline techniques is not enough. Far from it. The world lurking behind a textbook’s toy domains has a way of complicating things, frustrating the engineer with unexpected obstacles, and challenging everybody’s notion of what exactly the induced classifier is supposed to do and why. Just as in any other field of technology, success is hard to achieve without a healthy dose of creativity.
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Kononenko, I., Bratko, I., & Kukar, M. (1998). Application of machine learning to medical diagnosis. In R. Michalski, I. Bratko, & M. Kubat (Eds.), Machine learning and data mining: Methods and applications. Chichester: Wiley.
Kubat, M., Pfurtscheller, G., & Flotzinger D. (1994). AI-based approach to automatic sleep classification. Biological Cybernetics, 79, 443–448. CrossRef
Kubat, M., Holte, R., & Matwin, S. (1998). Detection of oil-spills in radar images of sea surface. Machine Learning, 30, 195–215. CrossRef
Kubat, M., Koprinska, I., & Pfurtscheller, G. (1998). Learning to classify medical signals. In R. Michalski, I. Bratko, & M. Kubat (Eds.), Machine learning and data mining: Methods and applications. Chichester: Wiley.
Lewis, D. D. & Gale, W. A. (1994). A sequential algorithm for training text classifiers. In Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR’94), Dublin (pp. 3–12).
Mori, S, Suen, C. Y., & Yamamoto, K. (1992). Historical overview of OCR research and development. Proceedings of IEEE, 80, 1029–1058.
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