Andrew Skabar

2008
15	EE	Andrew Skabar: A Kernel-Based Technique for Direction-of-Change Financial Time Series Forecasting. ICCS (2) 2008: 441-449
2007
14	EE	Andrew Skabar, Narendra Juneja: A Kernel-Based Method for Semi-Supervised Learning. ACIS-ICIS 2007: 112-117
2006
13	EE	Andrew Skabar, Dennis Wollersheim, Tim Whitfort: Multi-label Classification of Gene Function using MLPs. IJCNN 2006: 2234-2240
2005
12	EE	Andrew Skabar: Application of Bayesian Techniques for MLPs to Financial Time Series Forecasting. Australian Conference on Artificial Intelligence 2005: 888-891
11	EE	Andrew Skabar: Application of Bayesian MLP Techniques to Predicting Mineralization Potential from Geoscientific Data. ICANN (2) 2005: 963-968
10	EE	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	EE	Andrew Skabar: Predicting the Distribution of Discrete Spatial Events Using Artificial Neural Networks. Australian Conference on Artificial Intelligence 2003: 567-577
6	EE	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	EE	Andrew Skabar, Ian Cloete: Neural Networks and Financial Trading and the Efficient Markets Hypothesis. ACSC 2002: 241-249
3	EE	Andrew Skabar: Augmenting Supervised Neural Classifier Training Using a Corpus of Unlabeled Data. KI 2002: 174-185
2000
2	EE	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

Coauthor Index

1	Kousick Biswas	[2]
2	Ian Cloete	[4]
3	Narendra Juneja	[14]
4	Anthony J. Maeder	[1] [2]
5	Binh Pham (Binh T. Pham)	[1] [2]
6	Tim Whitfort	[13]
7	Dennis Wollersheim	[13]

Colors in the list of coauthors