By N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia Micheli-Tzanakou
Read Online or Download Biometrics: Theory, Methods, and Applications (IEEE Press Series on Computational Intelligence) PDF
Similar computational mathematicsematics books
The aim of this quantity is to provide the foundations of the Augmented Lagrangian procedure, including a variety of functions of this technique to the numerical resolution of boundary-value difficulties for partial differential equations or inequalities coming up in Mathematical Physics, within the Mechanics of continuing Media and within the Engineering Sciences.
Computational fluid dynamics (CFD) and optimum form layout (OSD) are of useful value for plenty of engineering purposes - the aeronautic, motor vehicle, and nuclear industries are all significant clients of those applied sciences. Giving the state-of-the-art fit optimization for a longer diversity of purposes, this re-creation explains the equations had to comprehend OSD difficulties for fluids (Euler and Navier Strokes, but in addition these for microfluids) and covers numerical simulation ideas.
- Basics of Fluid Mechanics and Intro to Computational Fluid Dynamics
- Evolutionary Computation in Stochastic Environments
- The Mathematics of Derivatives: Tools for Designing Numerical Algorithms (Wiley Finance)
- Computational Physics: Problem Solving With Computers
- Numerical investigation of the non-reacting unsteady flow behind a disk stabilized burner with large blockage
- Numerische Loesung nichtlinearer Gleichungen
Additional info for Biometrics: Theory, Methods, and Applications (IEEE Press Series on Computational Intelligence)
N. Cristianini and J. S. Taylor, An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, New York, 2000. 47. S. Sch¨olkopf and A. Smola, Learning with Kernels: Support Vector Machines,Regularization, Optimization and Beyond, MIT Press, Cambridge, MA, 2002. 48. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, New York, 2004. 49. S. Mika, G. R¨atsch, J. Weston, B. -R. -H. Hu, J. Larsen, E. Wilson, and S.
Wang and X. Tang, A uniﬁed framework for subspace face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(9):1222–1228, 2004. 24. J. Ye, Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems, J. Mach. Learning Res. 6:483–502, 2005. 25. H. Park, M. Jeon, and J. B. Rosen, Lower dimensional representation of text data based on centroids and least squares, BIT 43(2):1–22, 2003. 26. P. Howland, M. Jeon, and H. Park, Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition, SIAM J.
Royal Stat. Soc. Ser. B (1):267–288, 1996. 69. L. Wang and X. Shen, On L1 -norm multiclass support vector machines: Methodology and theory, J. Am. Stat. Assoc. 102(478):583–594, 2007. 70. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, 1-Norm support vector machines, in Advances in Neural Information Processing Systems, 2003. 71. J. Ye, J. Chen, R. Janardan, and S. Kumar, Developmental stage annotation of Drosophila gene expression pattern images via an entire solution path for LDA, in ACM Transactions on Knowledge Discovery from Data, Special Issue on Bioinformatics, 2008.
Biometrics: Theory, Methods, and Applications (IEEE Press Series on Computational Intelligence) by N. V. Boulgouris, Konstantinos N. Plataniotis, Evangelia Micheli-Tzanakou