- **General Discussion of Machine Learning**
(*http://book.caltech.edu/bookforum/forumdisplay.php?f=105*)

- - **Regression and Classification Problems**
(*http://book.caltech.edu/bookforum/showthread.php?t=4313*)

Re: Regression and Classification ProblemsQuote:
There are works that try to systematically connection regression to classification. The one you see in SVM regression more or less follows from the idea of "loss symmetrization" (You can google for some related work back ten years ago). For bounded-range regression, there are works like John Langford and Bianca Zadrozny Estimating Class Membership Probabilities Using Classifier Learners AISTAT 2005 based on using classifiers to decide suitable "thresholds" within the range. In works that reduce regression to classification, another key issue is usually about whether the reduced problems are "easy enough" to be solved well by classifiers. For instance, classifying every bit of the real-valued target separately may be challenging for classifiers, because you'd essentially need a high-frequency function (i.e. complex classifier) for the low-order bits. Hope this helps. |

Re: Regression and Classification ProblemsThanks, Lin (is your last name your given name, in the Chinese style?).
Following the principle that a picture is worth a thousand words, I thought I would post a couple instead of 2000 words. Here is the classification error equivalent to mean square error regression (with a fairly crude quantisation to make it less painful to look at) http://i699.photobucket.com/albums/v...fa/L2error.png and here is the classification error emulation of the wacky but natural "bit error regression" (where the error function is proportional to the complement of the number of correct leading bits in the values). http://i699.photobucket.com/albums/v...biterror-1.png The above option may be entirely useless (although it can be dangerous to guess that), but a less crazy looking example is L1 regression error: http://i699.photobucket.com/albums/v.../L1error-1.png |

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