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Tag Archive for machine-learning

How to make the leap from classification to clustering

I have a clustering problem which I can’t seem to solve, although if I treat it as a labeled classification problem, I can solve it with satisfactory precision. Is there an elegant way to make the leap from being able to solve the classification problem, to being able to solve the clustering one?

Is it possible to get probabilities from a support vector machine?

From what I understand, SVM’s take a discrete number of x and y values from which to learn from, then when given new x values map it to one y value (category). Is it possible to use SVM’s or something similar to instead map x values to probabilities of y values?
Let me give you an example, say your x values are arrays of two integers: x = [[1,1],[1,0],[0,1],[0,0]], and you have two categories, a and b such that y = [a,a,b,b]. i.e. [1,1] and [1,0] map to a, [0,1] and [0,0] map to b. Given an x value of [1,0.9], the SVM would probably predict the y value to be the category a, given another x value [1,0.89], the SVM would probably still predict the y value to be a part of the a category.

Is it possible to get probabilities from a support vector machine?

From what I understand, SVM’s take a discrete number of x and y values from which to learn from, then when given new x values map it to one y value (category). Is it possible to use SVM’s or something similar to instead map x values to probabilities of y values?
Let me give you an example, say your x values are arrays of two integers: x = [[1,1],[1,0],[0,1],[0,0]], and you have two categories, a and b such that y = [a,a,b,b]. i.e. [1,1] and [1,0] map to a, [0,1] and [0,0] map to b. Given an x value of [1,0.9], the SVM would probably predict the y value to be the category a, given another x value [1,0.89], the SVM would probably still predict the y value to be a part of the a category.

Is it possible to get probabilities from a support vector machine?

From what I understand, SVM’s take a discrete number of x and y values from which to learn from, then when given new x values map it to one y value (category). Is it possible to use SVM’s or something similar to instead map x values to probabilities of y values?
Let me give you an example, say your x values are arrays of two integers: x = [[1,1],[1,0],[0,1],[0,0]], and you have two categories, a and b such that y = [a,a,b,b]. i.e. [1,1] and [1,0] map to a, [0,1] and [0,0] map to b. Given an x value of [1,0.9], the SVM would probably predict the y value to be the category a, given another x value [1,0.89], the SVM would probably still predict the y value to be a part of the a category.