Sha F, Lin Y, Saul LK, Lee DD. (2007). Multiplicative updates for nonnegative quadratic programming. Neural computation. 19 [PubMed]

See more from authors: Sha F · Lin Y · Saul LK · Lee DD

References and models cited by this paper

Allen JB, Berkley DA. (1979). Image method for efficiently simulating small room acoustics J Acoust Soc Am. 65

Bertsekas DP. (1999). Nonlinear programming (2nd ed).

Darroch JN, Ratcliff D. (1972). Generalized iterative scaling for log-linear models Ann Math Stat. 43

Dempster AP, Laird NM, Rubin DB. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 39

Diego JM, Tegmark M, Protopapas P, Sandvik HB. (2007). Combined reconstruction of weak and strong lensing data with WSLAP Month Not Royal Astro Soc. 375

Friess T, Cristianini N, Campbell C. (1998). The kernel adatron algorithm:A fast and simple learning procedure for support vector machine Proc. 15th International Conference on Machine Learning.

Kivinen J, Warmuth MK. (1997). Exponentiated gradient versus gradient descent for linear predictors Information And Computation. 132

Lee DD, Saul LK, Sha F. (2003). Multiplicative updates for nonnegative quadratic programming in support vector machines Advances in Neural Information Processing Systems 15 (Proceedings of NIPS02).

Lee DD, Saul LK, Sha F. (2003). Statistical signal processing with nonnegativity constraints Proc 8th Euro Conf Speech Communication and Technology. 2

Lee DD, Saul LK, Sha F. (2003). Multiplicative updates for large margin classifiers Proc 16th Ann Conf Comput Learn Theory.

Lee DD, Seung HS. (1999). Learning the parts of objects by non-negative matrix factorization. Nature. 401 [PubMed]

Lin Y, Lee DD, Saul LK. (2004). Nonnegative deconvolution for time of arrival estimation Proc Intl Conf Speech, Acoustics, Signal Process. 2

Platt J. (1999). Fast training of support vector machines using sequential minimal optimization Advances in kernel methods: Support vector learning.

Poggio T et al. (1997). Comparing support vector machines with gaussian kernels to radial basis function classiers IEEE Trans Signal Process. 45

Serafini T, Zanghirati G, Zanni L. (2005). Gradient projection methods for quadratic programs and applications in training support vector machines Optimization Methods And Software. 20

Seung HS, Lee DD. (2000). Algorithms for non-negative matrix factorization Advances in neural information processing systems. 13

Shawe-taylor J, Cristianini N. (2000). An introduction to support vector machines.

Singer Y, Bauer E, Koller D. (1997). Update rules for parameter estimation in Bayesian networks Proc 13th Ann Conf Uncertainty AI.

Vapnik V. (1998). Statistical Learning Theory.

Wright SJ. (1997). Primal-dual interior point methods.

Zangwill WJ. (1969). Nonlinear programming: A unified approach.

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.