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1. Particle Swarm Optimization in Exploratory Data Analysis

2. Extending Metric Multidimensional Scaling with Bregman Divergences

3. Independent Component Analysis Using Bregman Divergences

4. Class Imbalance Methods for Translation Initiation Site Recognition

5. Cross Entropy Methods

13. Introduction

14. Conclusions

16. Negative feedback as an organising principle for artificial neural networks

17. Feature Locations in Images

18. Bregman Divergences and the Self Organising Map

19. A Family of Novel Clustering Algorithms

20. The Topographic Neural Gas

21. Multi-layer Topology Preserving Mapping for K-Means Clustering

22. Bregman Divergences and Multi-dimensional Scaling

23. Immediate Reward Reinforcement Learning for Clustering and Topology Preserving Mappings

25. Topology-Preserving Mappings for Data Visualisation

26. Stochastic Weights Reinforcement Learning for Exploratory Data Analysis

27. Reinforcement Learning Reward Functions for Unsupervised Learning

28. Clustering with Reinforcement Learning

29. Visualising and Clustering Video Data

32. Simulation of Cooperation for Price Competition in Oligopolies

33. Twinned Topographic Maps for Decision Making in the Cockpit

34. Teamwork and Simulation in Hybrid Cognitive Architecture

35. Two Methods for Sparsifying Probabilistic Canonical Correlation Analysis

36. The Sphere-Concatenate Method for Gaussian Process Canonical Correlation Analysis

37. Local vs Global Models in Pong

38. The Topographic Product of Experts

39. An Intelligent Aircraft Landing Support System

40. Development of a Global and Integral Model of Business Management Using an Unsupervised Model

41. Strategy Selection in Games Using Co-evolution Between Artificial Immune Systems

42. Agent Wars with Artificial Immune Systems

44. A Kernel Method for Classification

45. Kernel Maximum Likelihood Hebbian Learning

46. Initialising Self-Organising Maps

48. Relevance and Kernel Self-Organising Maps

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