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https://www.aaai.org/Papers/ICML/2003/ICML03-004.pdf
Hidden Markov Support Vector Machines YaseminAltun [email protected] IoannisTsochantaridis [email protected] ThomasHofmann [email protected] DepartmentofComputerScience,BrownUniversity,Providence,RI02912USA Abstract This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913668/
Jun 01, 2010 · A hybrid supervised machine learning algorithm, hidden Markov support vector machines, was then used to classify the underlying state of each probe to ‘expression’ or ‘silence’ with the assumption that the consecutive state sequence was a heterogeneous Markov chain.Cited by: 10
https://www.cs.helsinki.fi/group/smart/teaching/58308109/niissaloPrint.pdf
Hidden Markov Support Vector Machines Label Sequence Learning Aija Niissalo main ref. Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann: Hidden Markov Support Vector Machines. Proc. ICML’2003 April 1, 2008 Aija Niissalo (Predicting Structured Data) Hidden Markov Support Vector Machines April 1, 2008 1 / 23
https://www.researchgate.net/publication/2853958_Hidden_Markov_Support_Vector_Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov ...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901317/
Hidden Markov Models . Arising from a state machine structure Hidden Markov Models (HMMs) comprise two stochastic processes. First, as illustrated in figure 2, a set of Q underlying states is used to describe a sequence of T O-dimensional feature vectors O t. These states q t ∈ {s i} i =1…Cited by: 29
http://smil.csie.ntnu.edu.tw/ppt/20070212_Winston_HM-SVM.pdf
Feb 12, 2007 · learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine (HM-SVM). • The learning procedure is discriminative and is based on a maximum/soft margincriterion. • It is possible to learn non-linear discriminant functions via klftikernel functions.
https://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-63.pdf
Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications Jerry Hong University of California, Berkeley Soda Hall, 2599 Hearst Ave Berkeley, CA 94720-1776 [email protected] ABSTRACT This paper presents two approaches in helping investors make better decisions. First, we discuss conventional methods,
http://www.cs.cornell.edu/people/tj/svm_light/svm_hmm.html
Support Vector Machine Learning for Interdependent and Structured Output Spaces. International Conference on Machine Learning (ICML), 2004. [Postscript] [PDF] Y. Altun, I. Tsochantaridis, T. Hofmann, Hidden Markov Support Vector Machines. International Conference on Machine …
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://www2.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-63.html
May 12, 2010 · Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications; ... we will discuss using artificial intelligence, such as Hidden Markov Models and Support Vector Machines, to help investors gather and compute enormous amount of data that will enable them to make informed decisions. ... Peter %T Analysis of Hidden Markov ...
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