Projects per year
Abstract
Logistic Regression (LR) is a workhorse of the statistics community and a state-of-the-art machine learning classifier. It learns a linear model from inputs to outputs trained by optimizing the Conditional Log-Likelihood (CLL) of the data. Recently, it has been shown that preconditioning LR using a Naive Bayes (NB) model speeds up LR learning many-fold. One can, however, train a linear model by optimizing the mean-square-error (MSE) instead of CLL. This leads to an Artificial Neural Network (ANN) with no hidden layer. In this work, we study the effect of NB preconditioning on such an ANN classifier. Optimizing MSE instead of CLL may lead to a lower bias classifier and hence result in better performance on big datasets. We show that this NB preconditioning can speed-up convergence significantly. We also show that optimizing a linear model with MSE leads to a lower bias classifier than optimizing with CLL. We also compare the performance to state-of-the-art classifier Random Forest.
Original language | English |
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Title of host publication | Advances in Knowledge Discovery and Data Mining |
Subtitle of host publication | 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19-22, Proceedings, Part I |
Editors | James Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang |
Place of Publication | Switzerland |
Publisher | Springer |
Pages | 341-353 |
Number of pages | 13 |
Volume | 1 |
ISBN (Electronic) | 9783319317533 |
ISBN (Print) | 9783319317526 |
DOIs | |
Publication status | Published - 2016 |
Event | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2016 - Auckland, New Zealand Duration: 19 Apr 2016 → 22 Apr 2016 Conference number: 20th http://pakdd16.wordpress.fos.auckland.ac.nz/ https://link.springer.com/book/10.1007/978-3-319-31753-3 (Proceedings) |
Publication series
Name | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |
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Publisher | Springer |
Volume | 9651 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2016 |
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Abbreviated title | PAKDD 2016 |
Country/Territory | New Zealand |
City | Auckland |
Period | 19/04/16 → 22/04/16 |
Internet address |
Keywords
- Artificial neural networks
- Conditional loglikelihood
- Logistic regression
- Mean-square-error
- Preconditioning
- WANBIA-C
Projects
- 2 Finished
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Combining generative and discriminative strategies to facilitate efficient and effective learning from big data
Webb, G. (Primary Chief Investigator (PCI))
Australian Research Council (ARC), Monash University
2/01/14 → 31/12/16
Project: Research
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Knowledge Discovery from Data in the Context of Prior Beliefs
Webb, G. (Primary Chief Investigator (PCI)) & Nicholson, A. (Chief Investigator (CI))
Australian Research Council (ARC), Monash University
15/07/12 → 31/10/15
Project: Research