Using Statistical Learning Theory to Improve Treatment Response for Metastatic Colorectal Carcinoma

Metastatic Colorectal Carcinoma ◽

Improve Treatment

Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory

BMC Genomics ◽

10.1186/1471-2164-11-s3-s15 ◽

2010 ◽

Vol 11 (Suppl 3) ◽

pp. S15 ◽

Cited By ~ 1

Author(s):

Walker H Land ◽

Dan Margolis ◽

Ronald Gottlieb ◽

Elizabeth A Krupinski ◽

Jack Y Yang

Keyword(s):

Colorectal Carcinoma ◽

Treatment Response ◽

Statistical Learning ◽

Learning Theory ◽

Metastatic Colorectal Carcinoma

Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning

International Journal of Computational Biology and Drug Design ◽

10.1504/ijcbdd.2010.034463 ◽

2010 ◽

Vol 3 (1) ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Walker H. Land <suffix>Jr.</suffix> ◽

Dan Margolis ◽

Ronald Gottlieb ◽

Jack Y. Yang ◽

Elizabeth A. Krupinski

Keyword(s):

Colorectal Carcinoma ◽

Treatment Response ◽

Statistical Learning ◽

Metastatic Colorectal Carcinoma

Universal Consistency and Bloat in GP Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint

Revue d intelligence artificielle ◽

10.3166/ria.20.805-827 ◽

2006 ◽

Vol 20 (6) ◽

pp. 805-827 ◽

Cited By ~ 7

Author(s):

Sylvain Gelly ◽

Olivier Teytaud ◽

Nicolas Bredeche ◽

Marc Schoenauer

Keyword(s):

Genetic Programming ◽

Statistical Learning ◽

Learning Theory ◽

Universal Consistency ◽

Theoretical Considerations

Practical use of statistical learning theory for modeling freezing point depression of electrolyte solutions: LSSVM model

Journal of Natural Gas Science and Engineering ◽

10.1016/j.jngse.2014.06.020 ◽

2014 ◽

Vol 20 ◽

pp. 414-421 ◽

Cited By ~ 32

Author(s):

Hamidreza Yarveicy ◽

Ali Kariman Moghaddam ◽

Mohammad M. Ghiasi

Keyword(s):

Statistical Learning ◽

Learning Theory ◽

Freezing Point ◽

Electrolyte Solutions ◽

Freezing Point Depression

Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826) ◽

Tuning of neural networks based on genetic algorithm and statistical learning theory

10.1109/icmlc.2004.1378565 ◽

2005 ◽

Author(s):

En-hui Zheng ◽

Min Yang

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Statistical Learning ◽

Learning Theory ◽

Statistical Learning Theory

Technometrics ◽

10.1080/00401706.1999.10485951 ◽

1999 ◽

Vol 41 (4) ◽

pp. 377-378 ◽

Cited By ~ 3

Author(s):

Yuhai Wu

Keyword(s):

Statistical Learning ◽

Learning Theory ◽

Application of statistical learning theory to predict corrosion rate of injecting water pipeline

9th IEEE International Conference on Cognitive Informatics (ICCI'10) ◽

10.1109/coginf.2010.5599754 ◽

2010 ◽

Cited By ~ 1

Author(s):

Huang Zhen ◽

Liu Hong ◽

Fan Mujiao ◽

Xu Chunbi

Keyword(s):

Corrosion Rate ◽

Statistical Learning ◽

Learning Theory ◽

Water Pipeline

Multiparameter Regularization for Construction of Extrapolating Estimators in Statistical Learning Theory

Multiscale Signal Analysis and Modeling ◽

10.1007/978-1-4614-4145-8_15 ◽

2012 ◽

pp. 347-366 ◽

Cited By ~ 2

Author(s):

Shuai Lu ◽

Sergiy Pereverzyev ◽

Sivananthan Sampath

Keyword(s):

Statistical Learning ◽

Learning Theory ◽

Lecture Notes in Control and Information Sciences - Recent Advances in Learning and Control ◽

Statistical Learning Theory: A Pack-based Strategy for Uncertain Feasibility and Optimization Problems

10.1007/978-1-84800-155-8_1 ◽

2007 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Teodoro Alamo ◽

Roberto Tempo ◽

Eduardo F.

Keyword(s):

Statistical Learning ◽

Learning Theory ◽

Optimization Problems ◽

Early damage warning of railway bridge in time-domain based on statistical learning theory

Bridge Maintenance, Safety, Management and Life Extension ◽

10.1201/b17063-61 ◽

2014 ◽

pp. 437-444

Author(s):

D Shan ◽

Z Huang ◽

I Khan ◽

C Fu

Keyword(s):

Statistical Learning ◽

Learning Theory ◽

Time Domain ◽

Railway Bridge ◽

Early Damage