 | 2009 |
|---|
| 17 |       | Andrew Skabar:
Lag-Dependent Regularization for MLPs Applied to Financial Time Series Forecasting Tasks.
ICCS (2) 2009: 515-523 |
| 2008 |
|---|
| 16 | | Andrew Skabar:
A Kernel-Based Technique for Direction-of-Change Financial Time Series Forecasting.
ICCS (2) 2008: 441-449 |
| 15 | | Andrew Skabar:
Direction-of-Change Financial Time Series Forecasting Using Neural Networks: A Bayesian Approach.
World Congress on Engineering (Selected Papers) 2008: 515-524 |
| 2007 |
|---|
| 14 | | Andrew Skabar,
Narendra Juneja:
A Kernel-Based Method for Semi-Supervised Learning.
ACIS-ICIS 2007: 112-117 |
| 2006 |
|---|
| 13 | | Andrew Skabar,
Dennis Wollersheim,
Tim Whitfort:
Multi-label Classification of Gene Function using MLPs.
IJCNN 2006: 2234-2240 |
| 2005 |
|---|
| 12 | | Andrew Skabar:
Application of Bayesian Techniques for MLPs to Financial Time Series Forecasting.
Australian Conference on Artificial Intelligence 2005: 888-891 |
| 11 | | Andrew Skabar:
Application of Bayesian MLP Techniques to Predicting Mineralization Potential from Geoscientific Data.
ICANN (2) 2005: 963-968 |
| 10 | | Andrew Skabar:
Automatic MLP Weight Regularization on Mineralization Prediction Tasks.
KES (3) 2005: 595-601 |
| 2004 |
|---|
| 9 |  | Andrew Skabar:
An Objective Function Based on Bayesian Likelihoods of Necessity and Sufficiency For Concept Learning in the Absence of Labeled Counter-Examples.
IC-AI 2004: 634-640 |
| 8 | | Andrew Skabar:
Comparison of MLP and Bayesian Approaches on Mineral Prospectivity Mapping Tasks.
IC-AI 2004: 946-952 |
| 2003 |
|---|
| 7 | | Andrew Skabar:
Predicting the Distribution of Discrete Spatial Events Using Artificial Neural Networks.
Australian Conference on Artificial Intelligence 2003: 567-577 |
| 6 | | Andrew Skabar:
Single-Class Classification Augmented with Unlabeled Data: A Symbolic Approach.
Australian Conference on Artificial Intelligence 2003: 735-746 |
| 5 | | Andrew Skabar:
A GA-based Neural Network Weight Optimization Technique for Semi-Supervised Classifier Learning.
HIS 2003: 139-146 |
| 2002 |
|---|
| 4 | | Andrew Skabar,
Ian Cloete:
Neural Networks and Financial Trading and the Efficient Markets Hypothesis.
ACSC 2002: 241-249 |
| 3 | | Andrew Skabar:
Augmenting Supervised Neural Classifier Training Using a Corpus of Unlabeled Data.
KI 2002: 174-185 |
| 2000 |
|---|
| 2 | | Andrew Skabar,
Kousick Biswas,
Binh Pham,
Anthony J. Maeder:
Inductive Concept Learning in the Absence of Labeled Counter-Examples.
ACSC 2000: 220-226 |
| 1 | | Andrew Skabar,
Anthony J. Maeder,
Binh Pham:
A Classifier Fitness Measure Based on Bayesian Likelihoods: An Approach to the Problem of Learning from Positives Only.
PRICAI 2000: 177-187 |
