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Old 02-05-2014, 07:17 AM
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magdon magdon is offline
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Join Date: Aug 2009
Location: Troy, NY, USA.
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Default Re: does PLA works for the cases of Non-linear separable

You can run PLA but it will not converge. A better alternative is the pocket algorithm which you will see in chapter 3. Chapter 3 also describes several other algorithms which can be used for non-separable data, including linear regression, linear programming etc. These are algorithms that stick with the linear model and tolerate errors. The (hard margin) SVM also needs the data to be separable, but you could use the (soft margin) SVM.

The more complex algorithms you mention like neural networks can perform non-linear separation, which can also be accomplished with the linear model and the non-linear transform (see chapter 3).

Quote:
Originally Posted by netweavercn View Post
In Page 7, seems PLA has a prerequisite: linear separable. In many cases, usually you have thousands of data points, it is almost impossible to be linear separable because of the noise or maybe the nature is non-linear separable, in this case, does PLA work? if not, any suggestion? i.e. Nerual Network? SVM?
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