J. Utans

Statistical Physics, Mixtures of Distributions, and the EM Algorithm

Alan L. Yuille, Joachim Utans, Paul E. Stolorz, 1994, Neural Computation.

Mixture Models and the Em Algorithm for Object Recognition within Compositional Hierarchies Part 1: Recognition

J. Utans, 1993 .

Selecting neural network architectures via the prediction risk: application to corporate bond rating prediction

J. Utans, J. Moody, J. Moody, 1991, Proceedings First International Conference on Artificial Intelligence Applications on Wall Street.

Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Prediction

John E. Moody, Joachim Utans, J. Moody, 1991, NIPS.

Neural networks for object recognition within compositional hierarchies

J. Utans, 1992 .

Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Predicti

John Moody, Joachim Utans, J. Moody, 1995, NIPS 1995.

Do European Brokers Add any Value Through Recommendations?

Campbell R. Harvey, J. Utans, Sandy Rattray, 2009 .

When European Analysts Disagree, Who Should You Pay Attention To?

Campbell R. Harvey, J. Utans, Sandy Rattray, 2010 .

A Non-convergent On-Line Training Algorithm for Neural Networks

Joachim Utans, J. Utans, 1997, IWANN.

Stopped training via algebraic online estimation of the expected test-set error

J. Utans, J. Utans, 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

Input variable selection for neural networks: application to predicting the U.S. business cycle

J. Utans, J. Moody, S. Rehfuss, 1995, Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr).