Information Theory and Statistical Learning

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Emmert-Streib, Frank (Επιμελητής έκδοσης), Dehmer, Matthias (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US, 2009.
• Θέματα: Computer science.; Coding theory.; Computers.; Computer science > Mathematics.; Artificial intelligence.; Control engineering.; Robotics.; Mechatronics.; Electrical engineering.; Computer Science.; Coding and Information Theory.; Artificial Intelligence (incl. Robotics).; Theory of Computation.; Mathematics of Computing.; Communications Engineering, Networks.; Control, Robotics, Mechatronics.

Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of co...

Πίνακας περιεχομένων:

• Algorithmic Probability: Theory and Applications
• Model Selection and Testing by the MDL Principle
• Normalized Information Distance
• The Application of Data Compression-Based Distances to Biological Sequences
• MIC: Mutual Information Based Hierarchical Clustering
• A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information
• Information Approach to Blind Source Separation and Deconvolution
• Causality in Time Series: Its Detection and Quantification by Means of Information Theory
• Information Theoretic Learning and Kernel Methods
• Information-Theoretic Causal Power
• Information Flows in Complex Networks
• Models of Information Processing in the Sensorimotor Loop
• Information Divergence Geometry and the Application to Statistical Machine Learning
• Model Selection and Information Criterion
• Extreme Physical Information as a Principle of Universal Stability
• Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler.