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An Improved Conjugate Gradient Scheme to the Solution of Least Squares SVM
2005
IEEE Transactions on Neural Networks
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The Least Square Support Vector Machines (LS-SVM) formulation corresponds to the solution of a linear system of equations. ...
Compared with the existing algorithm for LS-SVM, our approach is about twice as efficient. Numerical results using the proposed method are provided for comparisons with other existing algorithms. ...
Acknowledgments Wei Chu gratefully acknowledges the financial support provided by the National University of ...
doi:10.1109/tnn.2004.841785
pmid:15787157
fatcat:vrhcuj2qzbglnhrkdyfthayx3q
A least squares formulation for a class of generalized eigenvalue problems in machine learning
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
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In addition, the least squares formulation leads to efficient and scalable implementations based on the iterative conjugate gradient type algorithms. ...
In this paper, we show that under a mild condition, a class of generalized eigenvalue problems in machine learning can be formulated as a least squares problem. ...
Acknowledgments This work was supported by NSF IIS-0612069, IIS-0812551, CCF-0811790, and NGA HM1582-08-1-0016. ...
doi:10.1145/1553374.1553499
dblp:conf/icml/SunJY09
fatcat:crm2jkkfwnc5zgzy4dgjqf2q24
Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods
2022
Energies
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Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines ...
In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. ...
Acknowledgments: This work was supported by the Qatar National Library. Open access funding provided by the Qatar National Library. ...
doi:10.3390/en15176124
fatcat:x6x4lj7ttfc5tf6hwchdcccdrm
A hardware acceleration technique for gradient descent and conjugate gradient
2011
2011 IEEE 9th Symposium on Application Specific Processors (SASP)
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We show that the proposed accelerator can provide significant speedups for iterative versions of several applications and that for some applications such as least squares, it can substantially improve ...
To mitigate the performance loss from robustification, we present the design of a hardware accelerator and corresponding software support that accelerate gradient descent and conjugate gradient based iterative ...
ACKNOWLEDGEMENTS The authors would like to thank Joseph Sloan and the anonymous referees for their valuable feedback. This work was supported in part by NSF and GSRC. ...
doi:10.1109/sasp.2011.5941086
dblp:conf/sasp/KeslerDK11
fatcat:o4ahplwirnfoxmvnoiabza2m2a
Big data analytics with small footprint
2013
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13
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By co-designing all of these elements we achieve single-machine performance levels that equal or exceed reported cluster implementations for common benchmark problems. ...
We present several benchmark problems to show how the above elements combine to yield multiple orders-of-magnitude improvements for each problem. ...
Solving Miγi = βCi using an iterative method (conjugate gradient here) requires only black-box evaluation of Miγi for various query vectorŝ γi. ...
doi:10.1145/2487575.2487677
dblp:conf/kdd/CannyZ13
fatcat:qplx3uzxcnglve3zxxe3q53o6i
Robust linear and support vector regression
2000
IEEE Transactions on Pattern Analysis and Machine Intelligence
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solvable simple convex quadratic program for both linear and nonlinear support vector estimators. ...
Numerical test comparisons with these algorithms indicate the computational effectiveness of the new quadratic programming model for both linear and nonlinear support vector problems. ...
ACKNOWLEDGMENTS The research described in this Data Mining Institute Report 99-09, November 1999, was supported by the US National Science Foundation grants CCR-9729842 and CDA-9623632, by Air Force Office ...
doi:10.1109/34.877518
fatcat:qsjjtq3bmbctdpytypeqe33q3a
High Performance Scientific Computing Using FPGAs with IEEE Floating Point and Logarithmic Arithmetic for Lattice QCD
2006
2006 International Conference on Field Programmable Logic and Applications
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The recent development of large FPGAs along with the availability of a variety of floating point cores have made it possible to implement high-performance matrix and vector kernel operations on FPGAs. ...
In this paper we seek to evaluate the performance of FPGAs for real scientific computations by implementing Lattice QCD, one of the classic scientific computing problems. ...
Double precision conjugate gradient solver The double precision conjugate gradient solver requires double precision versions of the dot-product and vector scale-add operations. ...
doi:10.1109/fpl.2006.311191
dblp:conf/fpl/CallananGNP06
fatcat:jmrr2vbuxrelzly423zzrolnoi
GPU-based power flow analysis with Chebyshev preconditioner and conjugate gradient method
2014
Electric power systems research
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This work implemented a polynomial preconditioner Chebyshev preconditioner with graphic processing unit (GPU), and integrated a GPU-based conjugate gradient solver. ...
Results show that GPU-based Chebyshev preconditioner can reach around 46× speedup for the largest test system, and conjugate gradient can gain more than 4× speedup. ...
Acknowledgement This work was supported in part by US NSF grant ECCS-1128381. ...
doi:10.1016/j.epsr.2014.05.005
fatcat:ekb7abwty5ablj4s2s4y5we7c4
Kernel Conjugate Gradient for Fast Kernel Machines
2007
International Joint Conference on Artificial Intelligence
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We propose a novel variant of the conjugate gradient algorithm, Kernel Conjugate Gradient (KCG), designed to speed up learning for kernel machines with differentiable loss functions. ...
We establish an upper bound on the number of iterations for KCG that indicates it should require less than the square root of the number of iterations that standard conjugate gradient requires. ...
Acknowledgements The authors gratefully acknowledge the partial support of this research by the DARPA Learning for Locomotion contract. ...
dblp:conf/ijcai/RatliffB07
fatcat:hpiythdstbdmfn6phde6p4fnea
Context-Aware Energy Enhancements for Smart Mobile Devices
2014
IEEE Transactions on Mobile Computing
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learning algorithms in Section 4, including a list of pros and cons for each algorithm; (iv) Analysis and results for a new machine learning technique based on support vector machines; (v) Further description ...
overhead for each of the algorithms in Section 6.4, in which the algorithms are run on an actual mobile device. ...
Support Vector Machines Support Vector Machines (SVMs) have become quite popular in recent years. ...
doi:10.1109/tmc.2013.94
fatcat:mswk3fx3svbhzitu2z4u2jv2xi
Kernel learning at the first level of inference
2014
Neural Networks
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In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. ...
Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. ...
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/F010508/1 -Advancing Machine Learning Methodology for New Classes of Prediction Problems. ...
doi:10.1016/j.neunet.2014.01.011
pmid:24561452
fatcat:iszwkmrzabg57eksbfcmyruz7m
Efficient Kernel Machines Using the Improved Fast Gauss Transform
2004
Neural Information Processing Systems
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The computation and memory required for kernel machines with N training samples is at least O(N 2 ). ...
We present an approximation technique based on the improved fast Gauss transform to reduce the computation to O(N ). ...
Nail Gumerov for many discussions. We also gratefully acknowledge support of NSF awards 9987944, 0086075 and 0219681. ...
dblp:conf/nips/YangDD04
fatcat:ywidj35hbreexokmujoamxlsaq
An Iterative Solver Benchmark
2001
Scientific Programming
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We present a benchmark of iterative solvers for sparse matrices. ...
The implementer then has the freedom to improve parallel efficiency by optimising the implementation for a particular ordering. ...
The Conjugate and BiConjugate gradient methods (see Fig. 1 ) involve, outside the matrix-vector prod-uct and preconditioner application, only simple vector operations. ...
doi:10.1155/2001/527931
fatcat:uqlitne4gnfipab6efm7wrzvlm
MapReduce for Parallel Reinforcement Learning
[chapter]
2012
Lecture Notes in Computer Science
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Furthermore, we design parallel reinforcement learning algorithms to deal with large scale problems using linear function approximation, including model-based projection, least squares policy iteration ...
, temporal difference learning and recent gradient temporal difference learning algorithms. ...
squares policy iteration, temporal difference learning, and the very recent gradient TD algorithms. ...
doi:10.1007/978-3-642-29946-9_30
fatcat:jyoovu2waffuzd5v3bxqyjdffm
Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks
2020
Nanomaterials
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In this study, three efficient artificial intelligent models including group method of data handling (GMDH), least squares support vector machine (LSSVM), and artificial neural network (ANN) are proposed ...
The models are also optimized using nine optimization techniques, namely coupled simulated annealing (CSA), genetic algorithm (GA), Bayesian regularization (BR), scaled conjugate gradient (SCG), ant colony ...
The modified version of SVM approach; namely least squares support vector machine (LSSVM), uses least-squares results in the form of a principle to obtain the minimum structural risk [65] [66] [67] . ...
doi:10.3390/nano10050890
pmid:32384755
pmcid:PMC7279394
fatcat:ymbbgy464zfmrn45a3b3ixqxti
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