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Part I - Incorporating Temporal Effects into ICA Models: 1. Hidden Markov Independent Component Analysis 2. Particle Filters for Non-Stationary ICA.- Part II - Considering the Validity of the Independence Assumption: 3. The Independence Assumption: Analysing the Independence of the Components by Topography 4. The Independence Assumption: Dependent Component Analysis.- Part III - Ensemble Learning and Applications to Nonlinear ICA and Image Processing: 5. Ensemble Learning 6. Bayesian Nonlinear Independent Component Analysis by Multi-Layer Perceptrons 7. Ensemble Learning for Blind Image Separation and Deconvolution.- Part IV - Data Analysis and Applications: 8. Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions 9. Blind Separation of Noisy Image Mixtures 10. Searching for Independence in Electromagnetic Brain Waves 11. ICA on Noisy Data: A Factor Analysis Approach 12. Analysis of Optical Imaging Data Using Weak Models and ICA 13. Independent Components in Text 14. Seeking Independence Using Biologically-Inspired Artificial Neural Networks.
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Add Advances In Independent Component Analysis, Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of t, Advances In Independent Component Analysis to the inventory that you are selling on WonderClubX
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Add Advances In Independent Component Analysis, Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of t, Advances In Independent Component Analysis to your collection on WonderClub |