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Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing
Connor Cooper, Geoffrey I. Webb, Daniel F. Schmidt
Bayesian network classifiers (BNCs) possess a number of properties desirable for a modern classifier: They are easily interpretable, highly scalable, and offer adaptable complexity…
MONSTER: Monash Scalable Time Series Evaluation Repository
Angus Dempster, Navid Mohammadi Foumani, Chang Wei Tan +6
We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a collection of large datasets for time series classification. The field of time series classification ha…
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data
Angus Dempster, Geoffrey I. Webb, Daniel F. Schmidt
Logistic regression is a ubiquitous method for probabilistic classification. However, the effectiveness of logistic regression depends upon careful and relatively computationally e…
Computing Marginal and Conditional Divergences between Decomposable Models with Applications
Loong Kuan Lee, Geoffrey I. Webb, Daniel F. Schmidt +1
The ability to compute the exact divergence between two high-dimensional distributions is useful in many applications but doing so naively is intractable. Computing the alpha-beta…
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Amr Abourayya, Jens Kleesiek, Kanishka Rao +4
In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been p…