104 research outputs found
2023 SDSU Data Science Symposium Presentation Abstracts
This document contains abstracts for presentations and posters 2023 SDSU Data Science Symposium
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with
arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author
Age Detection Through Keystroke Dynamics From User Authentication Failures
In this paper an incident response approach is proposed for handling detections of authentication failures in systems that employ dynamic biometric authentication and more specifically keystroke user recognition. The main component of the approach is a multi layer perceptron focusing on the age classification of a user. Empirical findings show that the classifier can detect the age of the subject with a probability that is far from the uniform random distribution, making the proposed method suitable for providing supporting yet circumstantial evidence during e-discovery
2019 SDSU Data Science Symposium Program
https://openprairie.sdstate.edu/ds_symposium_programs/1001/thumbnail.jp
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