Immunology as a Metaphor for Computational ... - Napier University

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Chapter 6. A Self-Organising SDM — SOSDM 145Figure 6.1: Kohonen map result for a clustered distribution which is uniform in theshaded areas. Due to a mismatch between the data distribution and the network topologythe data distribution is not well represented.Figure 6.2: Neural Gas result: the distribution of the units reflects the underlying datadistribution. There is no topological information, i.e. there are no neighbourhood connectionsFigure 6.3: Result of applying the Growing Neural Gas algorithm: the topology is verywell adapted to the data distribution. The structure consists of two clusters reflectingthe clustered data and all units lie in the regions in which the data actually comes from.
Chapter 6. A Self-Organising SDM — SOSDM 1461. Initialise the set A to contain 2 units, c 1 and c 2 , with reference vectorschosen randomly according to p £ ξ¥ .Initialise the connection set ¤ C U C2. Generate at random an input signal ξ according to p ξ¥ .£A A to the empty set:3. Determine the winner s 1 and the second nearest unit s 24. If a connection between s 1 and s 2 does not exist already, create it, andset the age of the connection between s 1 and s 2 to zero.5. Add the squared distance between the input signal and the winner to alocal error variable:6. Adapt the reference vectors of the winner and its direct topologicalneighbours by fractions of the total distance to the input signal.7. Increment the ages of all edges emanating from s 18. Remove the edges with an age greater than some parameter a max . If thisresults in units having no more emanating edges, then remove them aswell.9. If the number of input signals generated so far is an integer multiple ofa parameter λ, insert a new unit near the unit which has accumulatedmost error10. Decrease the error variables of all units11. If a stopping criterion has not been meet (e.g. net size or some performancemeasure) continue with step 2Figure 6.4: Fritzke’s GNG algorithm
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AbstractThe biological immune syste
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DeclarationI declare that this thes
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Table of Contents1 Introduction 11.
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5.3.1 Default Parameters . . . . .
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List of Figures2.1 ’Lock and Key
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List of Tables2.1 Structural and fu
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Chapter 1IntroductionThe study of b
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Chapter 1. Introduction 3scientists
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Chapter 1. Introduction 5Thus, the
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Chapter 1. Introduction 7of the cha
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Chapter 1. Introduction 94. Analysi
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Chapter 2. Background 11paratopeepi
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Chapter 2. Background 13antigenepit
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£Chapter 2. Background 15low the m
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Chapter 2. Background 17Antigene 0p
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Chapter 2. Background 19maintained
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E £ ρ¥ £Chapter 2. Background 2
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Chapter 2. Background 23genetically
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Chapter 2. Background 251. Choose a
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Chapter 2. Background 27non-interbr
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Chapter 2. Background 29been made i
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Chapter 2. Background 31patterns wi
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Chapter 2. Background 33reported on
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Chapter 2. Background 35Immunos 81
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Chapter 2. Background 37recognition
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Chapter 2. Background 391. Create a
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Chapter 2. Background 41environment
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Chapter 2. Background 43a)input dat
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Chapter 2. Background 45antibody wi
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Chapter 3. Immune Systems for Sched
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Page 204 and 205: Chapter 7. Conclusion 189Clustering
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Appendix BExperimental results obta
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Appendix B. Experimental results ob
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Bibliography 203[Cooke and Hunt, 19
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Bibliography 205[Farmer et al., 198
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Bibliography 207[Hart et al., 1998]
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Bibliography 209[Kohonen, 1982b] Ko
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Bibliography 211[Potter and De Jong
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Bibliography 213[Timmis et al., 199
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