Andrew Skabar

30 papers B 5C 1Misc 5Journal 4Unranked 15
YearRankTypeTitle / Venue / Authors
2019 B conf
Restricted Boltzmann Machines: an Eigencentrality-based Approach.
IJCNN
Andrew Skabar
2018 J jnl
Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from Mixed-Variable Data: Method and Applications.
CoRR
Andrew Skabar
2017 Misc conf
Clustering Mixed-Attribute Data using Random Walk.
ICCS
Andrew Skabar
2016 Misc conf
Random vector generation from mixed-attribute datasets using random walk.
WSC
Andrew Skabar
2015 — conf
Graph-Based Collaborative Filtering Using Rating Nodes: A Solution to the High Ratings/Low Ratings Problem.
Australasian Conference on Artificial Intelligence
Alphan Culha, Andrew Skabar
2013 J jnl
Clustering Sentence-Level Text Using a Novel Fuzzy Relational Clustering Algorithm.
IEEE Trans. Knowl. Data Eng.
Andrew Skabar, Khaled Abdalgader
2012 J jnl
Unsupervised similarity-based word sense disambiguation using context vectors and sentential word importance.
ACM Trans. Speech Lang. Process.
Khaled Abdalgader, Andrew Skabar
2010 B conf
Density estimation-based document categorization using von Mises-Fisher kernels.
IJCNN
Andrew Skabar, Shahmeer Memon
2010 — conf
Improving Sentence Similarity Measurement by Incorporating Sentential Word Importance.
Australasian Conference on Artificial Intelligence
Andrew Skabar, Khaled Abdalgader
2010 — conf
Short-Text Similarity Measurement Using Word Sense Disambiguation and Synonym Expansion.
Australasian Conference on Artificial Intelligence
Khaled Abdalgader, Andrew Skabar
2010 J jnl
Supervised Similarity-Based Classification using a Generative Graph-Based Model.
Aust. J. Intell. Inf. Process. Syst.
Andrew Skabar
2009 — conf
Evolutionary Intelligence and Communication in Societies of Virtually Embodied Agents.
ACAL
Binh Nguyen, Andrew Skabar
2009 — conf
Lag-Dependent Regularization for MLPs Applied to Financial Time Series Forecasting Tasks.
ICCS (2)
Andrew Skabar
2008 — conf
A Kernel-Based Technique for Direction-of-Change Financial Time Series Forecasting.
ICCS (2)
Andrew Skabar
2008 — conf
Direction-of-Change Financial Time Series Forecasting Using Neural Networks: A Bayesian Approach.
World Congress on Engineering (Selected Papers)
Andrew Skabar
2007 — conf
A Kernel-Based Method for Semi-Supervised Learning.
ACIS-ICIS
Andrew Skabar, Narendra Juneja
2006 B conf
Multi-label Classification of Gene Function using MLPs.
IJCNN
Andrew Skabar, Dennis Wollersheim, Tim Whitfort
2005 — conf
Application of Bayesian MLP Techniques to Predicting Mineralization Potential from Geoscientific Data.
ICANN (2)
Andrew Skabar
2005 — conf
Application of Bayesian Techniques for MLPs to Financial Time Series Forecasting.
Australian Conference on Artificial Intelligence
Andrew Skabar
2005 — conf
Automatic MLP Weight Regularization on Mineralization Prediction Tasks.
KES (3)
Andrew Skabar
2004 Misc conf
An Objective Function Based on Bayesian Likelihoods of Necessity and Sufficiency For Concept Learning in the Absence of Labeled Counter-Examples.
IC-AI
Andrew Skabar
2004 Misc conf
Comparison of MLP and Bayesian Approaches on Mineral Prospectivity Mapping Tasks.
IC-AI
Andrew Skabar
2003 C conf
A GA-based Neural Network Weight Optimization Technique for Semi-Supervised Classifier Learning.
HIS
Andrew Skabar
2003 B conf
Mineral potential mapping using feed-forward neural networks.
IJCNN
Andrew Skabar
2003 — conf
Predicting the Distribution of Discrete Spatial Events Using Artificial Neural Networks.
Australian Conference on Artificial Intelligence
Andrew Skabar
2003 — conf
Single-Class Classification Augmented with Unlabeled Data: A Symbolic Approach.
Australian Conference on Artificial Intelligence
Andrew Skabar
2002 Misc conf
Augmenting Supervised Neural Classifier Training Using a Corpus of Unlabeled Data.
KI
Andrew Skabar
2002 — conf
Neural Networks and Financial Trading and the Efficient Markets Hypothesis.
ACSC
Andrew Skabar, Ian Cloete
2000 B conf
A Classifier Fitness Measure Based on Bayesian Likelihoods: An Approach to the Problem of Learning from Positives Only.
PRICAI
Andrew Skabar, Anthony J. Maeder, Binh Pham
2000 — conf
Inductive Concept Learning in the Absence of Labeled Counter-Examples.
ACSC
Andrew Skabar, Kousick Biswas, Binh Pham, Anthony J. Maeder