Σεμινάριο Τομέα Μαθηματικών ΣΕΜΦΕ 29.03 - Daniel Schmidt (Monash University, Australia)

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ΑΝΑΚΟΙΝΩΣΗ

Ομιλία του κ. Daniel Schmidt (Associate Professor of Computer Science at
the Department of Data Science and AI, Monash University, Australia,
https://research.monash.edu/en/persons/daniel-schmidt) στο Σεμινάριο του
Τομέα Μαθηματικών ΣΕΜΦΕ, την ερχόμενη

Παρασκευή 29 Μαρτίου, στις 13:00 στην αίθουσα Σεμιναρίων του Τομέα
Μαθηματικών ΣΕΜΦΕ.

Τίτλος και περίληψη ακολουθούν.

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Title: Prevalidated ridge regression as a highly-efficient drop-in
replacement for logistic regression for high-dimensional data

Abstract: Linear models are widely used in classification and are
particularly effective for high-dimensional data where linear decision
boundaries/separating hyperplanes are often effective for separating
classes, even for complex data. A recent example of a technique
effectively utilising linear classifiers is the ROCKET family of
classifiers for time series classification. One reason that the ROCKET
family is so fast is due to its use of a linear classifier based around
standard squared-error ridge regression. Fitting a linear model based on
squared-error is significantly faster and more stable than fitting a
standard regularised multinomial logistic regression based on
logarithmic-loss (i.e., regularised maximum likelihood), as in the
latter case the solutions can only be found via a numerical search.
While fast, one drawback of using squared-error ridge-regression is that
it is unable to produce probabilistic predictions. I will demonstrate
some very recent work on how to use regular ridge-regression to train
L2-regularized multinomial logistic regression models for very large
numbers of features, including choosing a suitable degree of
regularization, with a time complexity that is no greater than single
ordinary least-squares fit. This in contrast to logistic regression,
which requires a full refit for every value of regularisation parameter
considered, and every fold used for cross-validation. Using our new
approach allows for models based on linear classifier technology to
provide well calibrated probabilistic predictions with minimal
additional computational overhead. If time permits, I will also discuss
some thoughts on when such linear classifiers would be expected to
perform well.