Deterministic and Statistical Methods in Machine Learning
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Deterministic and Statistical Methods in Machine Learning
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Beschreibung
Object Recognition via Local Patch Labelling.- Multi Channel Sequence Processing.- Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis.- Extensions of the Informative Vector Machine.- Efficient Communication by Breathing.- Guiding Local Regression Using Visualisation.- Transformations of Gaussian Process Priors.- Kernel Based Learning Methods: Regularization Networks and RBF Networks.- Redundant Bit Vectors for Quickly Searching High-Dimensional Regions.- Bayesian Independent Component Analysis with Prior Constraints: An Application in Biosignal Analysis.- Ensemble Algorithms for Feature Selection.- Can Gaussian Process Regression Be Made Robust Against Model Mismatch?.- Understanding Gaussian Process Regression Using the Equivalent Kernel.- Integrating Binding Site Predictions Using Non-linear Classification Methods.- Support Vector Machine to Synthesise Kernels.- Appropriate Kernel Functions for Support Vector Machine Learning with Sequences of Symbolic Data.- Variational Bayes Estimation of Mixing Coefficients.- A Comparison of Condition Numbers for the Full Rank Least Squares Problem.- SVM Based Learning System for Information Extraction.
Produktdetails
- Typ:
- Buch
- Einband:
- Taschenbuch
- Edition:
- 1
- Sprachen:
-
Original:
Englisch
- Seitenzahl:
-
356
- Herausgeber:
-
Springer
- Veröffentlichungsdatum:
-
2005-10-11
- ISBN / EAN:
-
9783540290735
- Gewicht:
-
605 g
- Maße:
-
16/24/2 cm
- GPSR-Herstellerreferenz:
-
[email protected]
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