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https://www.sciencedirect.com/science/article/pii/S0957417405001715
Support Vector Machines (SVMs) are a general class of statistical learning architectures that perform structural risk minimization on a nested set structure of separating hyperplanes (Cristianini and Shawe-Taylor, 2000, Schölkopf and Smola, 2002, and Vapnik 1999).Consider a binary classification problem with M patterns (x → i, y i), i=1,…,M where x → i ∈ R d and y i ∈{−1, 1}.Cited by: 110
http://is.tuebingen.mpg.de/fileadmin/user_upload/files/publications/ESwA_Response%20Modeling%20with%20Support%20Vector%20Machines_2715%5B1%5D.pdf
Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in
https://www.semanticscholar.org/paper/Response-modeling-with-support-vector-machines-Shin-Cho/51de6911ab30eeffca5b7775aa620036a03cc6e8/figure/5
Table 3 SVM models: the number of patterns selected from NPPS slightly varies with the given set of each fold, thus it is represented as an average over the five reduced training sets. - "Response modeling with support vector machines"
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.6842
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Support Vector Machine (SVM) employs Structural Risk Minimization (SRM) principle to generalize better than conventional machine learning methods employing the traditional Empirical Risk Minimization (ERM) principle. When applying SVM to response modeling in direct marketing, however, one has to deal …
http://s-space.snu.ac.kr/bitstream/10371/6181/3/Response%20Modeling%20with%20SVR.pdf
Response modeling, which predicts whether each customer will respond or how much each customer will spend based on the database of customers, becomes a key factor of direct marketing. In previous researches, several classiflcation approaches, include Support Vector Machines (SVM) and Neural Networks (NN), have been applied for response modeling.
https://www.semanticscholar.org/paper/Response-modeling-with-support-vector-machines-Shin-Cho/51de6911ab30eeffca5b7775aa620036a03cc6e8/figure/13
Fig. 10. Top-Decile profit and Weighted-Decile profit: R*-SVM is depicted as a bar while S-SVM is represented as a dotted reference line. - "Response modeling with support vector machines"
https://machinelearningmastery.com/support-vector-machines-for-machine-learning/
Support Vector Machines (Kernels) The SVM algorithm is implemented in practice using a kernel. The learning of the hyperplane in linear SVM is done by transforming the problem using some linear algebra, which is out of the scope of this introduction to SVM.
http://www.ipipan.waw.pl/~sj/pdf/upsvm.pdf
Uplift modeling, in contrast, allows for the use of a control dataset and aims at explicitly modeling the difference in outcome probabilities between the two groups, thus being able to identify cases for which the outcome of the action will be truly positive, neutral or negative. In this paper we present Uplift Support Vector Machines
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