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Andrew Skabar
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2010 – 2019
- 2019
- [c25]Andrew Skabar:
Restricted Boltzmann Machines: an Eigencentrality-based Approach. IJCNN 2019: 1-8 - 2018
- [i1]Andrew Skabar:
Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from Mixed-Variable Data: Method and Applications. CoRR abs/1809.07006 (2018) - 2017
- [c24]Andrew Skabar:
Clustering Mixed-Attribute Data using Random Walk. ICCS 2017: 988-997 - 2016
- [c23]Andrew Skabar:
Random vector generation from mixed-attribute datasets using random walk. WSC 2016: 1096-1107 - 2015
- [c22]Alphan Culha, Andrew Skabar:
Graph-Based Collaborative Filtering Using Rating Nodes: A Solution to the High Ratings/Low Ratings Problem. Australasian Conference on Artificial Intelligence 2015: 136-148 - 2013
- [j3]Andrew Skabar, Khaled Abdalgader:
Clustering Sentence-Level Text Using a Novel Fuzzy Relational Clustering Algorithm. IEEE Trans. Knowl. Data Eng. 25(1): 62-75 (2013) - 2012
- [j2]Khaled Abdalgader, Andrew Skabar:
Unsupervised similarity-based word sense disambiguation using context vectors and sentential word importance. ACM Trans. Speech Lang. Process. 9(1): 2:1-2:21 (2012) - 2010
- [j1]Andrew Skabar:
Supervised Similarity-Based Classification using a Generative Graph-Based Model. Aust. J. Intell. Inf. Process. Syst. 12(3) (2010) - [c21]Khaled Abdalgader, Andrew Skabar:
Short-Text Similarity Measurement Using Word Sense Disambiguation and Synonym Expansion. Australasian Conference on Artificial Intelligence 2010: 435-444 - [c20]Andrew Skabar, Khaled Abdalgader:
Improving Sentence Similarity Measurement by Incorporating Sentential Word Importance. Australasian Conference on Artificial Intelligence 2010: 466-475 - [c19]Andrew Skabar, Shahmeer Memon:
Density estimation-based document categorization using von Mises-Fisher kernels. IJCNN 2010: 1-8
2000 – 2009
- 2009
- [c18]Binh Nguyen, Andrew Skabar:
Evolutionary Intelligence and Communication in Societies of Virtually Embodied Agents. ACAL 2009: 73-85 - [c17]Andrew Skabar:
Lag-Dependent Regularization for MLPs Applied to Financial Time Series Forecasting Tasks. ICCS (2) 2009: 515-523 - 2008
- [c16]Andrew Skabar:
A Kernel-Based Technique for Direction-of-Change Financial Time Series Forecasting. ICCS (2) 2008: 441-449 - [c15]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
- [c14]Andrew Skabar, Narendra Juneja:
A Kernel-Based Method for Semi-Supervised Learning. ACIS-ICIS 2007: 112-117 - 2006
- [c13]Andrew Skabar, Dennis Wollersheim, Tim Whitfort:
Multi-label Classification of Gene Function using MLPs. IJCNN 2006: 2234-2240 - 2005
- [c12]Andrew Skabar:
Application of Bayesian Techniques for MLPs to Financial Time Series Forecasting. Australian Conference on Artificial Intelligence 2005: 888-891 - [c11]Andrew Skabar:
Application of Bayesian MLP Techniques to Predicting Mineralization Potential from Geoscientific Data. ICANN (2) 2005: 963-968 - [c10]Andrew Skabar:
Automatic MLP Weight Regularization on Mineralization Prediction Tasks. KES (3) 2005: 595-601 - 2004
- [c9]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 - [c8]Andrew Skabar:
Comparison of MLP and Bayesian Approaches on Mineral Prospectivity Mapping Tasks. IC-AI 2004: 946-952 - 2003
- [c7]Andrew Skabar:
Predicting the Distribution of Discrete Spatial Events Using Artificial Neural Networks. Australian Conference on Artificial Intelligence 2003: 567-577 - [c6]Andrew Skabar:
Single-Class Classification Augmented with Unlabeled Data: A Symbolic Approach. Australian Conference on Artificial Intelligence 2003: 735-746 - [c5]Andrew Skabar:
A GA-based Neural Network Weight Optimization Technique for Semi-Supervised Classifier Learning. HIS 2003: 139-146 - 2002
- [c4]Andrew Skabar, Ian Cloete:
Neural Networks and Financial Trading and the Efficient Markets Hypothesis. ACSC 2002: 241-249 - [c3]Andrew Skabar:
Augmenting Supervised Neural Classifier Training Using a Corpus of Unlabeled Data. KI 2002: 174-185 - 2000
- [c2]Andrew Skabar, Kousick Biswas, Binh Pham, Anthony J. Maeder:
Inductive Concept Learning in the Absence of Labeled Counter-Examples. ACSC 2000: 220-226 - [c1]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
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