Abstract
Suppose one is confronted with a medical classification problem. What trustworthy technique should one then use to solve it? Support vector machines (SVMs) are known to be a smart choice. But how can one make a personal, more flexible implementation of the learning engine that makes them run that well? And how does one open the black box behind their predicted diagnosis and explain the reasoning to the otherwise reluctant fellow physicians? Alternatively, one could choose to develop a more versatile evolutionary algorithm(EA) to tackle the classification task towards a potentially more understandable logic of discrimination.
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Keywords
- Support Vector Machine
- Evolutionary Algorithm
- Inference Engine
- Evolutionary Algorithm Optimization
- Class Prototype
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2014 Springer International Publishing Switzerland
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Stoean, C., Stoean, R. (2014). Introduction. In: Support Vector Machines and Evolutionary Algorithms for Classification. Intelligent Systems Reference Library, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-06941-8_1
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DOI: https://doi.org/10.1007/978-3-319-06941-8_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06940-1
Online ISBN: 978-3-319-06941-8
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