David Tcheng

Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX (2008)

Ball, Nicholas M., Brunner, Robert J., Myers, Adam D., Strand, Natalie E., Alberts, Stacey L., Tcheng, David

We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data...

Robust Machine Learning Applied to Astronomical Datasets II: Quantifying Photometric Redshifts for Quasars Using Instance-Based Learning (2006)

Ball, Nicholas M., Brunner, Robert J., Myers, Adam D., Strand, Natalie E., Alberts, Stacey L., Tcheng, David, ...

We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in...

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees (2006)

Ball, Nicholas M., Brunner, Robert J., Myers, Adam D., Tcheng, David

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with...

Building robust learning systems by combining induction and optimization (1989)

David Tcheng, Bruce Lambert, Stephen C-y, Lu Larry Rendell

Each concept description language and search strategy has an inherent inductive bias, a preference for some hypotheses over others. No single inductive bias performs optimally on all problems. This...

Layered concept-learning and dynamically-variable bias management (1987)

Larry Rendell, Raj Seshu, David Tcheng

Concept learning is inherently complex. Without severe constraint or inductive "bias, " the general problem is intractable. While most learning systems have been designed with...