I build intelligent decision support systems for a living, and I found this to be an excellent primer on how to apply a range of patterns to improve the quality, costs, and robustness of these kinds of systems. With the evolution of elastic resources, it is now possible to provide a number of more targeted algorithms rather than a one size fits all approach. The craft is in how to combine these targeted approaches to get a better result, and the text is excellent at introducing the patterns and providing real examples of recognition, biometrics, and security systems built on these patterns. The author introduces three levels of patterns that build on the previous level. The first order patterns provide general approaches to combining multiple sources of knowledge, and include one pattern that demonstrates how combining algorithms can produce emergent results not seen by the individual algorithms. The second order patterns are the heart of the discussion, as they provide specific approaches to combining the outputs in creative ways with weighted analysis and intelligent transformations. The third order patterns are about the feedback loop to learn over time.
I enjoyed this book because it provided easy to understand patterns that provided the foundation for more complex approaches, and showed how the patterns built on each other. It provided examples that were easy to understand in image or voice recognition, biometrics, and security which made it concrete rather than many abstract examples. The author is well-versed in the application of these patterns, and provides insightful perspectives on when and how to apply the patterns.
Meta-Algorithmics: Patterns for Robust, Low Cost, High Quality Systems (英语) 精装
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