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Minimum Divergence Methods in Statistical Machine Learning

From an Information Geometric Viewpoint

Shinto Eguchi,Osamu Komori

Mathematics and Statistics (R0)

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Langue :English
ISBN :9784431569220
Date de parution :16 févr. 2023
Éditeur :Springer Japan

Marque éditoriale :Springer
Format de fichier :EPUB3
Taille de fichier :10.19 MB
Options de téléchargement :EPUB3 (Adobe DRM)

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Vos paramètres de confidentialité

Minimum Divergence Methods in Statistical Machine Learning

Résumé

À propos de ce livre

Détails du livre

Comment lire

Dans cette série

Minimum Gamma-Divergence for Regression and Classification Problems

Lectures semblables

Comprehensive Chemometrics

Time Series Analysis: Methods and Applications

Information Geometry and Its Applications

Introduction to Functional Data Analysis

Statistical Inference Based on the likelihood

Quantifying Life

The Skew-Normal and Related Families

Mixed Models

Statistical Inference for Fractional Diffusion Processes

Matrix Information Geometry

Chemometrics

Non-Linear Time Series

System Identification

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Statistics for Long-Memory Processes

Chemometrics

Modern Statistical Methods for Astronomy

Artificial Intelligence and Causal Inference

Forging Connections between Computational Mathematics and Computational Geometry

Nonlinear and Stochastic Climate Dynamics

Mathematics for the Life Sciences

Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

A Course in Mathematical Statistics and Large Sample Theory

Accuracy Verification Methods

Object Oriented Data Analysis

Compositional Data Analysis

Introduction to Statistical Machine Learning

Scaling, Fractals and Wavelets

Statistical Inference for Financial Engineering

Analysis of Multivariate and High-Dimensional Data

Information Theory Models Of Instabilities In Critical Systems

Research Papers in Statistical Inference for Time Series and Related Models

Computational Information Geometry

Essays on the Extended Evolutionary Synthesis

Long-Memory Processes

Inference for Diffusion Processes

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Large-Scale Inverse Problems and Quantification of Uncertainty

Patterns Identification and Data Mining in Weather and Climate

Stochastic Biomathematical Models

Applied Matrix and Tensor Variate Data Analysis

Advances and Challenges in Space-time Modelling of Natural Events

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Big Data in Omics and Imaging

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Introduction to Mathematics for Computational Biology

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Introduction to Mathematical Systems Theory

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Optimal Experimental Design for Non-Linear Models

Methodology and Applications of Statistics

Quantitative Analysis and Modeling of Earth and Environmental Data

Fractional and Multivariable Calculus

Verifying Calculations - Forty Years On

Soft Computing Approach to Pattern Classification and Object Recognition

Introduction to Turbulent Dynamical Systems in Complex Systems

Big Data in Omics and Imaging

Mathematical and Statistical Models and Methods in Reliability

Predicting the Future

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Stochastic Analysis of Biochemical Systems

Algebraic Approach to Data Processing

Consciousness, Cognition and Crosstalk: The Evolutionary Exaptation of Nonergodic Groupoid Symmetry-Breaking

Principles of Data Assimilation

Optimal Control of Stochastic Difference Volterra Equations

New Trends in the Applications of Differential Equations in Sciences

Astrostatistical Challenges for the New Astronomy

Optimization