Programming collective intelligence : building smart web 2.0 applications
This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features--crawlers, indexers, query engines, and the PageRank algorithm Optimization algorith...
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| 格式: | 图书 |
| 语言: | Undetermined |
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Beijing
O'Reilly
2007
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| Thư viện lưu trữ: | Trung tâm Học liệu Trường Đại học Cần Thơ |
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| 总结: | This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features--crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in adataset Evolving intelligence for problem solving--how a computer develops its skill by improving its own code the more it plays a game |
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