Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent MachinesSpringer Science & Business Media, 2013年3月14日 - 307 頁 Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains. |
搜尋書籍內容
第 1 到 5 筆結果,共 43 筆
第 ix 頁
... Unsupervised Learning . On - Line Unsupervised Learning - General 12 25 26 28 29 Principles 31 2.2 ECOS for On - Line Clustering 39 2.3 Self - Organising Maps - SOMS 49 2.4 Evolving Self - Organising Maps ( ESOM ) 52 2.5 Summary and ...
... Unsupervised Learning . On - Line Unsupervised Learning - General 12 25 26 28 29 Principles 31 2.2 ECOS for On - Line Clustering 39 2.3 Self - Organising Maps - SOMS 49 2.4 Evolving Self - Organising Maps ( ESOM ) 52 2.5 Summary and ...
第 6 頁
... unsupervised , supervised and reinforce- ment learning models . They employ a number of methods , such as evolving clus- tering , evolving self - organising maps , evolving fuzzy neural networks , knowledge manipulation , and structure ...
... unsupervised , supervised and reinforce- ment learning models . They employ a number of methods , such as evolving clus- tering , evolving self - organising maps , evolving fuzzy neural networks , knowledge manipulation , and structure ...
第 7 頁
... unsupervised / reinforcement learning , knowledge - based learning , statistical learning , open struc- ture and others are defined and illustrated . The chapter material is organised in the following sections : • Evolving processes ...
... unsupervised / reinforcement learning , knowledge - based learning , statistical learning , open struc- ture and others are defined and illustrated . The chapter material is organised in the following sections : • Evolving processes ...
第 12 頁
... unsupervised learning in which the synaptic strength ( weight ) is increased if both the source and the destination neurons become simultaneously activated . It is expressed as : Wij ( t + 1 ) = wij ( t ) + coioj ( 1.3 ) where wij ( t ) ...
... unsupervised learning in which the synaptic strength ( weight ) is increased if both the source and the destination neurons become simultaneously activated . It is expressed as : Wij ( t + 1 ) = wij ( t ) + coioj ( 1.3 ) where wij ( t ) ...
第 14 頁
... Unsupervised learning ( b ) Supervised learning ( c ) Reinforcement learning ( d ) Combined learning ( a ) Individual , development - based learning ( b ) Evolutionary learning , ( population - based learning over generations ) ( c ) ...
... Unsupervised learning ( b ) Supervised learning ( c ) Reinforcement learning ( d ) Combined learning ( a ) Individual , development - based learning ( b ) Evolutionary learning , ( population - based learning over generations ) ( c ) ...
內容
9 | |
4 | 26 |
2 | 34 |
3 | 42 |
4 | 51 |
3 | 53 |
1 | 57 |
Evolving Fuzzy Neural Networks EFUNN | 63 |
Evolving NeuroFuzzy Inference Systems | 99 |
Systems DENFIS | 107 |
6 | 120 |
Motivation and Implementation Issues | 143 |
8 | 165 |
in Spoken Languages | 209 |
OnLine Adaptive Speech Recognition | 229 |
5 | 240 |
Knowledge Manipulation in Evolving Fuzzy Neural | 75 |
OnLine Evaluation Feature Modification | 85 |
2 | 91 |
Evolving Systems for Integrated MultiModal Information | 257 |
Extended Glossary | 291 |
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常見字詞
activation adaptive aggregation analysis applied Artificial Neural Networks backpropagation bioinformatics calculated cell Chapter classification cluster centres connection weights created data examples data points data set data vectors defined DENFIS distance dynamic ECOS EFUNN ESOM evolutionary evolving connectionist systems evolving fuzzy neural evolving processes evolving system Futschik fuzzy input fuzzy neural networks fuzzy output fuzzy rules gene expression genetic algorithms hyperspheres inference system information processing input data input space input vector intelligent Kasabov KBNN knowledge language layer learning mode learning process learning system Māori Mel scale membership degrees membership functions modules neuro-fuzzy neurons number of examples off-line on-line learning on-line mode optimisation parameter values phoneme prediction presented problem space proteins prototype pruning quantisation radius RBF networks receptive field represent rule extraction rule nodes sequence signal speech recognition structure subsystem supervised learning task techniques tion unsupervised words