728 REFERENCES
Webb, A. R. (1994). Functional approximation by
feed-forward networks: a least-squares approach
to generalisation.IEEE Transactions on Neural
Networks 5 (3), 363–371.
Weisstein, E. W. (1999).CRC Concise Encyclopedia
of Mathematics. Chapman and Hall, and CRC.
Weston, J. and C. Watkins (1999). Multi-class sup-
port vector machines. In M. Verlysen (Ed.),Pro-
ceedings ESANN’99, Brussels. D-Facto Publica-
tions.
Whittaker, J. (1990).Graphical Models in Applied
Multivariate Statistics. Wiley.
Widrow, B. and M. E. Hoff (1960). Adaptive
switching circuits. InIRE WESCON Convention
Record, Volume 4, pp. 96–104. Reprinted in An-
derson and Rosenfeld (1988).
Widrow, B. and M. A. Lehr (1990). 30 years of adap-
tive neural networks: perceptron, madeline, and
backpropagation.Proceedings of the IEEE 78 (9),
1415–1442.
Wiegerinck, W. and T. Heskes (2003). Fractional
belief propagation. In S. Becker, S. Thrun, and
K. Obermayer (Eds.),Advances in Neural Infor-
mation Processing Systems, Volume 15, pp. 455–
- MIT Press.
Williams, C. K. I. (1998). Computation with infi-
nite neural networks.Neural Computation 10 (5),
1203–1216.
Williams, C. K. I. (1999). Prediction with Gaussian
processes: from linear regression to linear pre-
diction and beyond. In M. I. Jordan (Ed.),Learn-
ing in Graphical Models, pp. 599–621. MIT
Press.
Williams, C. K. I. and D. Barber (1998). Bayesian
classification with Gaussian processes. IEEE
Transactions on Pattern Analysis and Machine
Intelligence 20 , 1342–1351.
Williams, C. K. I. and M. Seeger (2001). Using the
Nystrom method to speed up kernel machines. In
T. K. Leen, T. G. Dietterich, and V. Tresp (Eds.),
Advances in Neural Information Processing Sys-
tems, Volume 13, pp. 682–688. MIT Press.
Williams, O., A. Blake, and R. Cipolla (2005).
Sparse Bayesian learning for efficient visual
tracking.IEEE Transactions on Pattern Analysis
and Machine Intelligence 27 (8), 1292–1304.
Williams, P. M. (1996). Using neural networks to
model conditional multivariate densities.Neural
Computation 8 (4), 843–854.
Winn, J. and C. M. Bishop (2005). Variational mes-
sage passing.Journal of Machine Learning Re-
search 6 , 661–694.
Zarchan, P. and H. Musoff (2005).Fundamentals of
Kalman Filtering: A Practical Approach(Sec-
ond ed.). AIAA.