Company
Philosophy
Directors
Robert W. Davis
Jon Ferguson
Staats M. Pellett
Leora Tilden
Thomas Tilden
Hal Woolley
Employees
Tim Andersen >>
William L. Crowley
Ullysses A. Eoff
Marc Footen
Debbie Garrett
Jeff Habig
Richard D. Newman
Tim Otter
Mason Vail
David Zuercher
Funding
Openings


Tim Andersen

Position
Senior Scientist

Education
B.S., Computer Science, Brigham Young University
M.S., Brigham Young University
Ph.D., Computer Science, Brigham Young University

Background
Dr. Andersen has significant industry experience in the field of document recognition. Dr. Andersen helped to develop an artificial neural network (ANN)-based OCR system as well as newspaper document segmentation algorithms, local adaptive thresholding (LAT) routines (a modification of Niblack's LAT algorithm that uses a secondary adaptive threshold to determine window size), and proprietary noise and scratch removal routines.

In September 2001, Dr. Andersen joined the faculty in the Computer Science Department at Boise State University. Dr. Andersen's areas of expertise include Neural Networks, Machine Learning, Genetic Algorithms, Pattern Recognition, Artificial Intelligence, and Computational Complexity. Dr. Andersen is actively pursuing research topics in the areas of: 1) biologically inspired methods of computation; 2) the use of ANNs for image segmentation, image region identification and OCR; 3) ANN architecture selection, in particular, he is interested in optimal ANN architecture selection for voting techniques such as bagging and boosting. His research in this area is studying the ability of ANNs with different architectures to learn and generalize using teacher networks of varying complexity and training set sizes; and 4) ANN training algorithms.

Publications
Andersen, T., R. Newman, and T. Otter (2009). Shape Homeostasis in Virtual Embryos. Artificial Life, Spring 2009, Vol. 15, No. 2, pp. 161-183.

Andersen, T., R. Newman, and T. Otter (2006). Development of virtual embryos with emergent self-repair. Technical Report FS-06-03, Proceedings of the AAAI Fall 2006 Symposium on Developmental Systems (Arlington, VA), pp. 16-23.

Andersen, T., T. Otter, C. Petschulat, U. Eoff, T. Menten, R. Davis, and B. Crowley (2005) A biologically-derived approach to tissue modeling. (MMVR 13 - Long Beach, CA) IOS Press, Amsterdam (J Westwood et al, eds.) Studies in Technology and Informatics 111:15-21.

Andersen T. L. and Wei Zhang (2003) Features for neural net based region identification of newspaper documents. Accepted for publication by The International Conference on Document Analysis and Recognition.

Andersen T. L. (2002) Removing decision surface skew using complimentary inputs. The International Joint Conference on Neural Networks.

Andersen T. L., D. Rimer and T. R. Martinez (2001) Optimal artificial neural network architecture selction for voting. The International Joint Conference on Neural Networks.

Rimer D., Andersen T. L. and T. R. Martinez (2001) Speedy learning: improving learning speed for large data sets. The International Joint Conference on Neural Networks.

Andersen T. L. and T. R. Martinez (2001) DMP3: A dynamic multi-layer perceptron construction algorithm. The International Journal of Neural Systems, Vol. 11, No. 2, April 2001, pp. 145-166.

Andersen T. L. and Martinez T.R. (1999) The little neuron that could. Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'99.

Andersen T. L. and T. R. Martinez (1998) Constructing Higher Order Perceptrons with Genetic Algorithms, Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'98, pp. 1920-1925

Andersen T. L. and T. R. Martinez (1995) NP-Completeness of Minimum Rule Sets, Proceedings of the 10th International Symposium on Computer and Information Sciences, pp. 411-418, 1995.




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