user11869

154 reputation

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visits	member for	1 year, 6 months
	seen	Nov 5 '12 at 14:37
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	154 reputation	bio	website		visits	member for	1 year, 6 months
5 badges		location			seen	Nov 5 '12 at 14:37

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Jun 6	awarded	Popular Question
Oct 28	awarded	Commentator
Oct 28	comment	How to use libSVM for one-class SVM problems? my training data consists of the instances from the class I want to identify, and I know most real-world data will NOT fall into that class. Should I set the value of nu to be smaller (e.g., less than 0.5?)
Oct 28	accepted	How to use libSVM for one-class SVM problems?
Oct 28	asked	How to use libSVM for one-class SVM problems?
Oct 28	comment	SVM with only one type of label @mbq, can you be more specific? The input to the learning machine is a feature vector, and the output is the (x,y,z) coordinates of the cutting point, is this what you mean?
Oct 27	asked	SVM with only one type of label
Oct 25	comment	libSVM for unbalanced data @Bitwise, what would you do in this case then?
Oct 25	awarded	Supporter
Oct 25	accepted	libSVM for unbalanced data
Oct 25	asked	libSVM for unbalanced data
Oct 22	comment	probablistic output for binary SVM classification Thanks. One more question, is there a c/c++ library for the Gaussian process classification?
Oct 22	accepted	probablistic output for binary SVM classification
Oct 22	asked	probablistic output for binary SVM classification
Oct 20	awarded	Scholar
Oct 20	accepted	a question on multiplicative SMV kernel
Oct 19	comment	a question on multiplicative SMV kernel I plan to make k1 a Gaussian kernel and k2 a RBF kernel. According to its tutorial, it seems libSVM only allows you choose one kernel type out of linear, polynomial, rbf and sigmoid. How do I tell the library I want a multiplicative kernel? Please excuse me if this question is too basic, I just quickly went through a book on SVM yesterday
Oct 19	asked	a question on multiplicative SMV kernel
Dec 8	comment	Understanding similarity sensitive hashing algorithm in AdaBoost I actually did contact the author before I post my question here. But I haven't heard anything back yet
Dec 8	comment	Understanding similarity sensitive hashing algorithm in AdaBoost Is the goal to minimize the exponential loss or maximize it? The paper said A and b should be chosen such that the exponential loss is minimized, while your goal is to maximize it. But anyway, the point here is to increase the weights of mis-classified examples, am I correct?