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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1930709
Sep 20, 2011 · Nonlinear Support Vector Machines Can Systematically Identify Stocks with High and Low Future ReturnsCited by: 8
https://www.semanticscholar.org/paper/Nonlinear-support-vector-machines-can-identify-with-Huerta-Corbacho/ee7dfe9335163564555d270baf538093ec407a02
Nonlinear support vector machines can systematically identify stocks with high and low future returns
http://biocircuits.ucsd.edu/huerta/AF_Huerta.pdf
/ Nonlinear support vector machines can systematically identify stocks with high and low future returns 47. The SVM function is trained such that f(x) is larger or equal than 1 if x belongs to class +1, and smaller or equal than 1 when it belongs to class 1.
http://disi.unitn.it/~passerini/teaching/2011-2012/MachineLearning/slides/16_nonlinear_svm/handouts.pdf
Non-linear Support Vector Machines feature map: X!H • is a function mapping each example to a higher dimensional space H •Examples xare replaced with their feature mapping ( x) •The feature mapping should increase the expressive power of the representation (e.g. introducing features which are combinations of input features)
https://towardsdatascience.com/the-complete-guide-to-support-vector-machine-svm-f1a820d8af0b
Jul 29, 2019 · That is why the support vector classifier was introduced as an extension of the maximal margin classifier, which can be applied in a broader range of cases. Finally, support vector machine is simply a further extension of the support vector classifier to accommodate non-linear class boundaries.Author: Marco Peixeiro
http://u.cs.biu.ac.il/~haimga/Teaching/AI/saritLectures/svm.pdf
2. Nonlinear Support Vector Machines • Note that the only way the data points appear in (the dual form of) the training problem is in the form of dot products x i ·x j. • In a higher dimensional space, it is very likely that a linear separator can be constructed. • We map …
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.
https://course.ccs.neu.edu/cs5100f11/resources/jakkula.pdf
Tutorial on Support Vector Machine (SVM) Vikramaditya Jakkula, School of EECS, Washington State University, Pullman 99164. Abstract: In this tutorial we present a brief introduction to SVM, and we discuss about SVM from published papers, workshop materials & material collected from books and material available online on
http://past.rinfinance.com/agenda/2016/talk/PedroAlexander.ppt
Portfolio selection with support vector machines in low economic perspectives in emerging markets. Economic Computation & Economic Cybernetics Studies & Research, 49 (4). Huerta, Ramon, Fernando Corbacho, and Charles Elkan. "Nonlinear support vector machines can systematically identify stocks with high and low future returns." Algorithmic Finance
https://pythonmachinelearning.pro/classification-with-support-vector-machines/
Support Vector Machines. The goal of support vector machines (SVMs) is to find the optimal line (or hyperplane) that maximally separates the two classes! (SVMs are used for binary classification, but can be extended to support multi-class classification). Mathematically, we can write the equation of that decision boundary as a line.
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