Scalable Learning of Bayesian Network Classifiers
Speaker: Professor Geoff Webb, Head of the Centre for Research in Intelligent Systems, Monash University
Date: Thursday 22 May 2014
Time: 2.00pm to 3.00pm
Location: Blackfriars Campus, Room CC05.01.05
Seminar Chairman: Professor Longbing CAO, Director, Advanced Analytics Institute (AAi) Longbing.Cao@uts.edu.au
Abstract:
I present our work on highly-scalable out-of-core techniques for learning well-calibrated Bayesian network classifiers. Our techniques are based on a novel hybrid generative and discriminative learning paradigm. These algorithms
- provide straightforward mechanisms for managing the bias-variance trade-off
- have training time that is linear with respect to training set size,
- require as few as one and at most four passes through the training data,
- allow for incremental learning,
- are embarrassingly parallelisable,
- support anytime classification,
- provide direct well-calibrated prediction of class probabilities,
- can learn using arbitrary loss functions,
- support direct handling of missing values, and
- exhibit robustness to noise in the training data.
Despite their computationally efficiency the new algorithms deliver classification accuracy that is competitive with state-of-the-art in-core discriminative learning techniques.
Bio:
Geoff Webb is a Professor of Information Technology Research in the Faculty of Information Technology at Monash University, where he heads the Centre for Research in Intelligent Systems. Prior to Monash he held appointments at Griffith University and then Deakin University, where he received a personal chair. His primary research areas are machine learning, data mining, user modelling and computational structural biology. He is known for his contribution to the debate about the application of Occam's razor in machine learning and for the development of numerous methods, algorithms and techniques for machine learning, data mining, user modelling and computational structural biology. His commercial data mining software, Magnum Opus, incorporates many techniques
from his association discovery research. Many of his learning algorithms are included in the widely-used Weka machine learning workbench. He is editor-in-chief of Data Mining and Knowledge Discovery, co-editor of the Springer Encyclopaedia of Machine Learning, a member of the advisory board of Statistical Analysis and Data Mining, a member of the editorial board of Machine Learning and was a foundation member of the editorial board of ACM Transactions on Knowledge Discovery from Data. He was co-PC Chair of the 2010 IEEE International Conference on Data Mining and co-General Chair of the 2012 IEEE International Conference on Data Mining. He has received the 2013 IEEE ICDM Service Award and a 2014 Australian Research Council Discovery Outstanding Researcher Award.
Overview to AAI seminar series
The Advanced Analytics Seminar Series presents the latest theoretical advancement and empirical experience in a broad range of interdisciplinary and business-oriented analytics fields. It covers topics related to data mining, machine learning, statistics, bioinformatics, behavior informatics, marketing analytics and multimedia analytics. It also provides a platform for the showcase of commercial products in ubiquitous advanced analytics. Speakers are invited from both academia and industry. It opens regularly on a week day at the garden-like UTS Blackfriars Campus. You are warmly welcome to attend this seminar series.
Thank you for your consideration to attending this quality Seminar by our visitor, Professor Geoff Webb, Head of the Centre for Research in Intelligent Systems, Monash University.