| Comparative Study Of Visualisation Methods For Temporal Data | 1 | 0.37 | 2012 |
| Self-Organizing Maps and Scale-Invariant Maps in Echo State Networks | 14 | 0.86 | 2011 |
| Reinforcement Learning for the N-Persons Iterated Prisoners' Dilemma | 1 | 0.36 | 2011 |
| Intelligent Data Engineering and Automated Learning - IDEAL 2010, 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings | 45 | 3.89 | 2010 |
| Intelligent Data Engineering and Automated Learning - IDEAL 2006, 7th International Conference, Burgos, Spain, September 20-23, 2006, Proceedings | 260 | 18.16 | 2009 |
| Intelligent Data Engineering and Automated Learning - IDEAL 2008, 9th International Conference, Daejeon, South Korea, November 2-5, 2008, Proceedings | 65 | 5.15 | 2008 |
| Immediate Reward Reinforcement Learning for Projective Kernel Methods | 2 | 0.40 | 2007 |
| Outlier identification with the Harmonic Topographic Mapping | 1 | 0.35 | 2006 |
| A Gaussian process latent variable model formulation of canonical correlation analysis | 7 | 0.66 | 2006 |
| Improving Artificial Intelligence In a Motocross Game | 20 | 2.29 | 2006 |
| Stochastic Processes for Canonical Correlation Analysis | 8 | 0.75 | 2006 |
| Comparing Gaussian Processes and Artificial Neural Networks for Forecasting | 1 | 0.36 | 2006 |
| Training an AI Player to play Pong Using the GTM | 1 | 0.53 | 2005 |
| Incrementally Learned Subjectivist Probabilities in Games | 0 | 0.34 | 2005 |
| Using Andrews Curves for Clustering and Sub-clustering Self-Organizing Maps | 2 | 0.36 | 2004 |
| A Gis And Web-Based Decision Support Tool For The Management Of Urban Soils | 2 | 0.60 | 2004 |
| Dynamic Strategy Creation and Selection Using Artificial Immune Systems | 4 | 0.85 | 2004 |
| An Extension of epsilon-Insensitive Hebbian Learning to Form a Non-Interfering Basis | 0 | 0.34 | 2003 |
| Finding Underlying Factors in Timeseries | 3 | 0.49 | 2002 |
| Forecasting using twinned principal curves | 2 | 0.46 | 2002 |
| Clustering in data space and feature space | 2 | 0.42 | 2002 |
| Identification of Visual Features Using a Neural Version of Exploratory Projection Pursuit | 0 | 0.34 | 2002 |
| Exploratory Correlation Analysis | 4 | 0.50 | 2002 |
| Multi-stream Exploratory Projection Pursuit for the Formation of Complex Cells Similar to Visual Cortical Neurons | 0 | 0.34 | 2002 |
| Sparse Kernel Canonical Correlation Analysis | 1 | 0.35 | 2001 |
| A comparison of sparse kernel principal component analysis methods | 1 | 0.38 | 2000 |
| Kernel exploratory projection pursuit | 1 | 0.45 | 2000 |
| The kernel self-organising map | 22 | 1.41 | 2000 |
| A Gneral Class of Neural Networks for Principal Component Analysis and Factor Analysis | 2 | 0.44 | 2000 |
| Generalised Canonical Correlation Analysis | 1 | 0.35 | 2000 |
| Probability prediction using support vector machines | 2 | 0.44 | 2000 |
| Dialect and Learned Systems of Communication in Multi-Agent Systems | 0 | 0.34 | 1999 |
| Data Mining using Unsupervised Neural Networks | 0 | 0.34 | 1999 |
| Noise to extract independent causes | 1 | 0.36 | 1999 |
| Learning Independent Causes of a Visual Data Set using the Rectified Gaussian Distribution | 0 | 0.34 | 1999 |
| Trends in Unsupervised Learning | 0 | 0.34 | 1999 |
| Modelling the Evolution of Linguistic Diversity | 10 | 1.80 | 1999 |
| Comparison of Kohonen, scale-invariant and GTM self-organising maps for interpretation of spectral data | 3 | 0.86 | 1999 |
| Neural networks which identify composite factors | 0 | 0.34 | 1999 |
| Cooperation in Oligopolies | 0 | 0.34 | 1999 |
| Canonical correlation analysis using artificial neural networks | 14 | 5.06 | 1998 |
| Invariant feature maps for analysis of orientations in image data | 1 | 0.43 | 1998 |
| Automatic Extraction of Phase and Frequency Information from Raw Voice Data | 2 | 0.55 | 1997 |
| Independence is far from normal | 2 | 1.55 | 1997 |
