Andrew Skabar

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15EEAndrew Skabar: A Kernel-Based Technique for Direction-of-Change Financial Time Series Forecasting. ICCS (2) 2008: 441-449
14EEAndrew Skabar, Narendra Juneja: A Kernel-Based Method for Semi-Supervised Learning. ACIS-ICIS 2007: 112-117
13EEAndrew Skabar, Dennis Wollersheim, Tim Whitfort: Multi-label Classification of Gene Function using MLPs. IJCNN 2006: 2234-2240
12EEAndrew Skabar: Application of Bayesian Techniques for MLPs to Financial Time Series Forecasting. Australian Conference on Artificial Intelligence 2005: 888-891
11EEAndrew Skabar: Application of Bayesian MLP Techniques to Predicting Mineralization Potential from Geoscientific Data. ICANN (2) 2005: 963-968
10EEAndrew Skabar: Automatic MLP Weight Regularization on Mineralization Prediction Tasks. KES (3) 2005: 595-601
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
7EEAndrew Skabar: Predicting the Distribution of Discrete Spatial Events Using Artificial Neural Networks. Australian Conference on Artificial Intelligence 2003: 567-577
6EEAndrew 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
4EEAndrew Skabar, Ian Cloete: Neural Networks and Financial Trading and the Efficient Markets Hypothesis. ACSC 2002: 241-249
3EEAndrew Skabar: Augmenting Supervised Neural Classifier Training Using a Corpus of Unlabeled Data. KI 2002: 174-185
2EEAndrew 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

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

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)