Simultaneous partitioning and modeling : a framework for learning from complex data
While a single learned model is adequate for simple prediction problems, it may not be sufficient to represent heterogeneous populations that difficult classification or regression problems often involve. In such scenarios, practitioners often adopt a "divide and conquer" strategy that se...
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| Format: | Others |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/2152/ETD-UT-2010-05-1284 |