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Decomposition Algorithms for Training Large-Scale Semiparametric Support Vector Machines [chapter]

Sangkyun Lee, Stephen J. Wright
2009 Lecture Notes in Computer Science  
We describe a method for solving large-scale semiparametric SVMs for regression problems.  ...  We compare our method with algorithms previously proposed for semiparametric SVMs, and show that it scales well as the number of training examples grows.  ...  SUMMARY Introduced an efficient algorithm for solving a generalized dual formulation of SVMs, which has multiple equality constraints.  ... 
doi:10.1007/978-3-642-04174-7_1 fatcat:wqsn32jv65h6boxmmboi2ulwhe

Parallel Semiparametric Support Vector Machines

Roberto Diaz-Morales, Harold Y. Molina-Bulla, Angel Navia-Vazquez
2011 The 2011 International Joint Conference on Neural Networks  
In this paper, we have developed and implemented with the multi-platform application programming interface OpenMP a method to train Semiparametric Support Vector Machines relying on Sparse Greedy Matrix  ...  Approximation (SGMA) and Iterated Re-Weighted Least Squares algorithm (IRWLS).  ...  CONCLUSIONS This paper proposes a parallel training algorithm for semiparametric support vector machines (PS-SVM), the main contribution is the use of quadtrees for the parallelization of matrix inversion  ... 
doi:10.1109/ijcnn.2011.6033259 dblp:conf/ijcnn/Diaz-MoralesMN11 fatcat:huj67nfmxfggzjzujxcorogvvu

Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting

Jelena Fiosina, Maksims Fiosins
2017 Applied Computational Intelligence and Soft Computing  
We present distributed parallel versions of some nonparametric and semiparametric regression models.  ...  The advantages of the parallelization of the algorithm are also provided.  ...  Here, we assume the decomposition of the explanatory variables X into two vectors, U and T.  ... 
doi:10.1155/2017/5134962 fatcat:luct7fqbybghffwluzoz623xhy

Incremental semiparametric inverse dynamics learning

Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori
2016 2016 IEEE International Conference on Robotics and Automation (ICRA)  
This paper presents a novel approach for incremental semiparametric inverse dynamics learning.  ...  This yields to an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models.  ...  Using SWIG [30] , iDynTree supports calling its algorithms in several programming languages, such as Python, Lua and Matlab.  ... 
doi:10.1109/icra.2016.7487177 dblp:conf/icra/CamorianoTRMN16 fatcat:2xgknh2onbd6vezrwr7wn6fjgm

Growing support vector classifiers with controlled complexity

E. Parrado-Hernández, I. Mora-Jiménez, J. Arenas-Garcı́a, A.R. Figueiras-Vidal, A. Navia-Vázquez
2003 Pattern Recognition  
Semiparametric Support Vector Machines have shown to present advantages with respect to nonparametric approaches, in the sense that generalization capability is further improved and the size of the machines  ...  We propose here an incremental procedure for Growing Support Vector Classiÿers, which serves to avoid an a priori architecture estimation or the application of a pruning mechanism after SVM training.  ...  Caamaño-Fernà andez for their valuable comments and support during the realization of this work. We would also like to thank Dr. F. Valverde-Albacete and Dr. H.  ... 
doi:10.1016/s0031-3203(02)00351-5 fatcat:fueg6iipt5eutebcg2dh6hufpm

Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning

Dan Ruan, Paul Keall
2010 Physics in Medicine and Biology  
The dimension reduction idea proposed in this work is closely related to feature extraction used in machine learning, particularly support vector machines.  ...  Accurate real-time prediction of respiratory motion is desirable for effective motion management in radiotherapy for lung tumor targets.  ...  The authors thank Drs Sonja Dieterich, Yelin Suh and Byung-Chul Cho for data collection and preparation, and Ms Elizabeth Roberts for editorial assistance.  ... 
doi:10.1088/0031-9155/55/11/002 pmid:20442460 pmcid:PMC2975024 fatcat:6yr25akrqrdddp2n5bvb7rjpgq

Copula Eigenfaces with Attributes: Semiparametric Principal Component Analysis for a Combined Color, Shape and Attribute Model [chapter]

Bernhard Egger, Dinu Kaufmann, Sandro Schönborn, Volker Roth, Thomas Vetter
2017 Communications in Computer and Information Science  
As a remedy, we use a semiparametric Gaussian copula model, where dependency and variance are modeled separately.  ...  Accounting for the joint dependency between all modalities leads to a more specific face model.  ...  This work was partially supported by the Swiss National Science Foundation, project 200021 146178: Copula Distributions in Machine Learning.  ... 
doi:10.1007/978-3-319-64870-5_5 fatcat:525k7pk3ybfh3fri5tsqfn35cu

Nonlinear kernel-based statistical pattern analysis

A. Ruiz, P.E. Lopez-de-Teruel
2001 IEEE Transactions on Neural Networks  
vector machine (SVM).  ...  Our results shed some light on the relative merit of feature spaces and inductive bias in the remarkable generalization properties of the support vector machine (SVM).  ...  ACKNOWLEDGMENT The authors would like to thank the Associate Editor and the referees for their careful reading and useful suggestions.  ... 
doi:10.1109/72.896793 pmid:18244360 fatcat:fty5q6myunagdoa2awkqofbdka

Training Support Vector Machines with Multiple Equality Constraints [chapter]

Wolf Kienzle, Bernhard Schölkopf
2005 Lecture Notes in Computer Science  
In this paper we present a primal-dual decomposition algorithm for support vector machine training.  ...  The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.  ...  Acknowledgments The authors would like to thank Oliver Chapelle, Alexander Zien and Kristin Bennett for useful comments.  ... 
doi:10.1007/11564096_21 fatcat:zuv2jpq6tzarbn3fwso4iwxyma

Semiparametric regression during 2003–2007

David Ruppert, M.P. Wand, Raymond J. Carroll
2009 Electronic Journal of Statistics  
We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.  ...  Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology -thus allowing  ...  Support vector machines (e.g.  ... 
doi:10.1214/09-ejs525 pmid:20305800 pmcid:PMC2841361 fatcat:37rhsccmvzeh3kiyuopffbw43q

Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems

Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
2022 IEEE Access  
Results are presented for a test case using load data from an electrical grid.  ...  Our forecasting algorithm is compared against state-of-the-art forecasting techniques not using additional explanatory variables and is shown to produce superior performance.  ...  This principle undergirds machine learning kernel methods such as support vector machines [61] , which relies on Cover's theorem [62] .  ... 
doi:10.1109/access.2022.3161438 fatcat:z663eq5izfaxrdabftqty6ll7y

Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [article]

Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
2021 arXiv   pre-print
Results are presented for a test case using load data from an electrical grid.  ...  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dynamic Mode Decomposition (DMD) in time delay coordinates.  ...  This principle undergirds machine learning kernel methods such as support vector machines [62] , which relies on Cover's theorem [63] .  ... 
arXiv:2010.04248v2 fatcat:yqbaj73mcrbddj5ddgsyr65ztq

Derivative-free online learning of inverse dynamics models [article]

Diego Romeres, Mattia Zorzi, Raffaello Camoriano, Silvio Traversaro, Alessandro Chiuso
2018 arXiv   pre-print
This paper discusses online algorithms for inverse dynamics modelling in robotics.  ...  Several model classes including rigid body dynamics (RBD) models, data-driven models and semiparametric models (which are a combination of the previous two classes) are placed in a common framework.  ...  approximation and relevance vector machine approach, see [27] .  ... 
arXiv:1809.05074v1 fatcat:yzaz2kazqvdlna7udqigap5nzy

Robust Speech Feature Extraction by Growth Transformation in Reproducing Kernel Hilbert Space

Shantanu Chakrabartty, Yunbin Deng, Gert Cauwenberghs
2007 IEEE Transactions on Audio, Speech, and Language Processing  
Mel-scale cepstral features and evaluated at noise levels between 0 and 30-dB signal-to-noise ratio.  ...  The performance of speech recognition systems depends on consistent quality of the speech features across variable environmental conditions encountered during training and evaluation.  ...  Deng of CLSP at JHU for their help in developing the baseline MFCC system using HTK.  ... 
doi:10.1109/tasl.2007.899285 fatcat:76e4ivmhqjhphn4dqspswdpkne

Real-time semiparametric regression for distributed data sets [article]

Jan Luts
2013 arXiv   pre-print
This paper proposes a method for semiparametric regression analysis of large-scale data which are distributed over multiple hosts.  ...  A website, realtime-semiparametric-regression.net, illustrates the use of the proposed method on United States domestic airline data in real-time.  ...  Acknowledgments This research was supported by Australian Research Council Discovery Project DP110100061. The author is grateful to Alan Huang and Matt Wand for their comments.  ... 
arXiv:1306.4734v1 fatcat:v6qz2epzjfe4hgdetoogfepqbu
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