The focus of this thesis is fast and robust adaptations of logistic
regression (LR) for data mining and high-dimensional classification
problems. LR is well-understood and widely used in the statistics,
machine learning, and data analysis communities. Its benefits include
a firm statistical foundation and a probabilistic model useful for
"explaining" the data. There is a perception that LR is slow,
unstable, and unsuitable for large learning or classification tasks.
Through fast approximate numerical methods, regularization to avoid
numerical instability, and an efficient implementation we will show
that LR can outperform modern algorithms like Support Vector Machines
(SVM) on a variety of learning tasks. Our novel implementation, which
uses a modified iteratively re-weighted least squares estimation
procedure, can compute model parameters for sparse binary datasets
with hundreds of thousands of rows and attributes, and millions or
tens of millions of nonzero elements in just a few seconds. Our
implementation also handles real-valued dense datasets of similar
size. We believe LR is a suitable building block for the discovery
systems as described above.