An Introduction to Support Vector Machines
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An Introduction to Support Vector Machines
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Re: An Introduction to Support Vector Machines
#2For a recent practical example of their usefulness:
This paper presents the Militarized Interstate Dispute (MID) 4.0 research design for updating the database from 2002-2010. By using global search parameters and fifteen international news sources, we collected a set of over 1.74 million documents from LexisNexis. Care was taken to create an all-inclusive set of search parameters as well as a sufficient and unbiased list of news sources. We classify these documents with two types of support vector machines (SVMs). Using inductive SVMs and a single training set, we remove 90.2% of documents from our initial set. Then, using year-specific training sets and transductive SVMs, we further reduce the number of human-coded stories by an additional 21.6%. The resulting classifications contain anywhere from 10,215 to 19,834 documents per year.
http://steventlandis.weebly.com/uploads/1/2/1/4/12144932/dor...
Re: An Introduction to Support Vector Machines
#3For a recent practical example of their usefulness: This paper presents the Militarized Interstate Dispute (MID) 4.0 research design for updating the database from 2002-2010. By using global search parameters and fifteen international news sources, we collected a set of over 1.74 million documents from LexisNexis. Care was taken to create an all-inclusive set of search parameters as well as a sufficient and unbiased…
5 years isn't a recent example. my impression is that deep nets ate everyone's lunch (including svm).
Re: An Introduction to Support Vector Machines
#4For a recent practical example of their usefulness: This paper presents the Militarized Interstate Dispute (MID) 4.0 research design for updating the database from 2002-2010. By using global search parameters and fifteen international news sources, we collected a set of over 1.74 million documents from LexisNexis. Care was taken to create an all-inclusive set of search parameters as well as a sufficient and unbiased…
5 years isn't a recent example. my impression is that deep nets ate everyone's lunch (including svm).
Not at all, deep nets are difficult to train and they need lot's of processing before they learn same kind of classifying features than ie. SVM has. So yes you can simulate SVM with deep networks but usually it's not very good solution.
SVM can be also used as part of the neural network such as in classifying layer
Re: An Introduction to Support Vector Machines
#5For a recent practical example of their usefulness: This paper presents the Militarized Interstate Dispute (MID) 4.0 research design for updating the database from 2002-2010. By using global search parameters and fifteen international news sources, we collected a set of over 1.74 million documents from LexisNexis. Care was taken to create an all-inclusive set of search parameters as well as a sufficient and unbiased…
5 years isn't a recent example. my impression is that deep nets ate everyone's lunch (including svm).
Deep nets ate everyone's hype. The lunch is still there. SVMs have many advantages over ANNs that recommend themselves to practical applications still.
Re: An Introduction to Support Vector Machines
#6I'd just like to note that instead of creating additional animosity between SVMs and deep nets, you could use both together. SVMs with hinge loss can be Yet-another-layer (tm) in your deep net, to be used when it provides better performance.
Re: An Introduction to Support Vector Machines
#7For a recent practical example of their usefulness: This paper presents the Militarized Interstate Dispute (MID) 4.0 research design for updating the database from 2002-2010. By using global search parameters and fifteen international news sources, we collected a set of over 1.74 million documents from LexisNexis. Care was taken to create an all-inclusive set of search parameters as well as a sufficient and unbiased…
5 years isn't a recent example. my impression is that deep nets ate everyone's lunch (including svm).
They really aren't appropriate for the same set of problems, even if there is some overlap.
There is a lot of buzz around deep learning right now, but the SNR isn't great.
Re: An Introduction to Support Vector Machines
#8For a recent practical example of their usefulness: This paper presents the Militarized Interstate Dispute (MID) 4.0 research design for updating the database from 2002-2010. By using global search parameters and fifteen international news sources, we collected a set of over 1.74 million documents from LexisNexis. Care was taken to create an all-inclusive set of search parameters as well as a sufficient and unbiased…
5 years isn't a recent example. my impression is that deep nets ate everyone's lunch (including svm).
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Re: An Introduction to Support Vector Machines
#9Can someone explain this part:
Imagine the new space we want:
z = x² + y²
Figure out what the dot product in that space looks like:
a · b = xa · xb + ya · yb + za · zb
a · b = xa · xb + ya · yb + (xa² + ya²) · (xb² + yb²)Re: An Introduction to Support Vector Machines
#10Earlier quoted context omitted.
5 years isn't a recent example. my impression is that deep nets ate everyone's lunch (including svm).
Deep nets ate everyone's hype. The lunch is still there. SVMs have many advantages over ANNs that recommend themselves to practical applications still.
What advantages do SVMs have at this stage?