Minimum Divergence Methods in Statistical Machine Learning

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  • Publisher : Springer Nature
  • Release : 29 May 2022
  • ISBN : 9784431569220
  • Page : 103 pages
  • Rating : 4.5/5 from 103 voters

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Minimum Divergence Methods in Statistical Machine Learning

Minimum Divergence Methods in Statistical Machine Learning
  • Author : Shinto Eguchi
  • Publisher : Springer Nature
  • Release Date : 2022-05-29
  • ISBN : 9784431569220
GET THIS BOOKMinimum Divergence Methods in Statistical Machine Learning

Information Theory and Statistical Learning

Information Theory and Statistical Learning
  • Author : Frank Emmert-Streib,Matthias Dehmer
  • Publisher : Springer Science & Business Media
  • Release Date : 2009
  • ISBN : 9780387848150
GET THIS BOOKInformation Theory and Statistical Learning

This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.

Minimum Divergence Methods in Statistical Machine Learning

Minimum Divergence Methods in Statistical Machine Learning
  • Author : Shinto Eguchi,Osamu Komori
  • Publisher : Springer
  • Release Date : 2022-04-18
  • ISBN : 4431569200
GET THIS BOOKMinimum Divergence Methods in Statistical Machine Learning

This book explores minimum divergence methods of statistical machine learning for estimation, regression, prediction, and so forth, in which we engage in information geometry to elucidate their intrinsic properties of the corresponding loss functions, learning algorithms, and statistical models. One of the most elementary examples is Gauss's least squares estimator in a linear regression model, in which the estimator is given by minimization of the sum of squares between a response vector and a vector of the linear subspace hulled

Statistical Inference

Statistical Inference
  • Author : Ayanendranath Basu,Hiroyuki Shioya,Chanseok Park
  • Publisher : CRC Press
  • Release Date : 2011-06-22
  • ISBN : 9781420099669
GET THIS BOOKStatistical Inference

In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by "Minimum Distance Estimation" is literally huge. Filling a statistical resource gap, Statistical Inference: The Minimum Distance Approach comprehensively overviews developments in density-based minimum distance inference for independently and identically distributed data. Extensions to other more complex models

Geometric Science of Information

Geometric Science of Information
  • Author : Frank Nielsen,Frédéric Barbaresco
  • Publisher : Springer Nature
  • Release Date : 2021-07-14
  • ISBN : 9783030802097
GET THIS BOOKGeometric Science of Information

This book constitutes the proceedings of the 5th International Conference on Geometric Science of Information, GSI 2021, held in Paris, France, in July 2021. The 98 papers presented in this volume were carefully reviewed and selected from 125 submissions. They cover all the main topics and highlights in the domain of geometric science of information, including information geometry manifolds of structured data/information and their advanced applications. The papers are organized in the following topics: Probability and statistics on Riemannian Manifolds; sub-Riemannian geometry and

Algorithmic Learning Theory

Algorithmic Learning Theory
  • Author : Ricard Gavalda,Klaus P. Jantke,Eiji Takimoto
  • Publisher : Springer Science & Business Media
  • Release Date : 2003-10-07
  • ISBN : 9783540202912
GET THIS BOOKAlgorithmic Learning Theory

This book constitutes the refereed proceedings of the 14th International Conference on Algorithmic Learning Theory, ALT 2003, held in Sapporo, Japan in October 2003. The 19 revised full papers presented together with 2 invited papers and abstracts of 3 invited talks were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on inductive inference, learning and information extraction, learning with queries, learning with non-linear optimization, learning from random examples, and online prediction.

Machine Learning for Signal Processing

Machine Learning for Signal Processing
  • Author : Max A. Little
  • Publisher : Oxford University Press, USA
  • Release Date : 2019-08-13
  • ISBN : 9780198714934
GET THIS BOOKMachine Learning for Signal Processing

This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications. Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the

Information Geometry and Its Applications

Information Geometry and Its Applications
  • Author : Nihat Ay,Paolo Gibilisco,František Matúš
  • Publisher : Springer
  • Release Date : 2018-11-03
  • ISBN : 9783319977980
GET THIS BOOKInformation Geometry and Its Applications

The book gathers contributions from the fourth conference on Information Geometry and its Applications, which was held on June 12–17, 2016, at Liblice Castle, Czech Republic on the occasion of Shun-ichi Amari’s 80th birthday and was organized by the Czech Academy of Sciences’ Institute of Information Theory and Automation. The conference received valuable financial support from the Max Planck Institute for Mathematics in the Sciences (Information Theory of Cognitive Systems Group), Czech Academy of Sciences’ Institute of Information Theory and Automation,

Multivariate Statistical Machine Learning Methods for Genomic Prediction

Multivariate Statistical Machine Learning Methods for Genomic Prediction
  • Author : Osval Antonio Montesinos López,Abelardo Montesinos López,José Crossa
  • Publisher : Springer Nature
  • Release Date : 2022-02-14
  • ISBN : 9783030890100
GET THIS BOOKMultivariate Statistical Machine Learning Methods for Genomic Prediction

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for

Learning Machine Translation

Learning Machine Translation
  • Author : Cyril Goutte,Nicola Cancedda,Marc Dymetman,George Foster
  • Publisher : MIT Press
  • Release Date : 2009
  • ISBN : 9780262072977
GET THIS BOOKLearning Machine Translation

How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.

Geometric Theory of Information

Geometric Theory of Information
  • Author : Frank Nielsen
  • Publisher : Springer Science & Business Media
  • Release Date : 2014-05-08
  • ISBN : 9783319053172
GET THIS BOOKGeometric Theory of Information

This book brings together geometric tools and their applications for Information analysis. It collects current and many uses of in the interdisciplinary fields of Information Geometry Manifolds in Advanced Signal, Image & Video Processing, Complex Data Modeling and Analysis, Information Ranking and Retrieval, Coding, Cognitive Systems, Optimal Control, Statistics on Manifolds, Machine Learning, Speech/sound recognition and natural language treatment which are also substantially relevant for the industry.

New Developments in Statistical Information Theory Based on Entropy and Divergence Measures

New Developments in Statistical Information Theory Based on Entropy and Divergence Measures
  • Author : Leandro Pardo
  • Publisher : MDPI
  • Release Date : 2019-05-20
  • ISBN : 9783038979364
GET THIS BOOKNew Developments in Statistical Information Theory Based on Entropy and Divergence Measures

This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, based on maximum likelihood estimators, as well as Wald’s statistics, likelihood ratio statistics and Rao’s score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning
  • Author : Masashi Sugiyama
  • Publisher : Morgan Kaufmann
  • Release Date : 2015-10-31
  • ISBN : 9780128023501
GET THIS BOOKIntroduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range

Statistics and Machine Learning Methods for EHR Data

Statistics and Machine Learning Methods for EHR Data
  • Author : Hulin Wu,Jose Miguel Yamal,Ashraf Yaseen,Vahed Maroufy
  • Publisher : CRC Press
  • Release Date : 2020-12-10
  • ISBN : 9781000260946
GET THIS BOOKStatistics and Machine Learning Methods for EHR Data

The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully

Density Ratio Estimation in Machine Learning

Density Ratio Estimation in Machine Learning
  • Author : Masashi Sugiyama,Taiji Suzuki,Takafumi Kanamori
  • Publisher : Cambridge University Press
  • Release Date : 2012-02-20
  • ISBN : 9780521190176
GET THIS BOOKDensity Ratio Estimation in Machine Learning

This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.