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Statistical Issues in Machine Learning Towards Reliable Split Selection and Variable Importance Measures

Statistical Issues in Machine Learning Towards Reliable Split Selection and Variable Importance Measures is een boek van Strobl, Carolin;

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Mathematical Literacy on Statistical Measures

specification of the learning content of statistical measures, an empirical reconstruction of students' learning processes towards statistical measures;

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Statistical and Machine Learning Approaches for Network Analysis

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches;

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Conformal Prediction for Reliable Machine Learning

financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory;

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Rank-Based Methods for Shrinkage and Selection

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based;

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Machine Learning in Bioinformatics

An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are;

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a;

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Big Data in Omics and Imaging, Two Volume Set

FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data;

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Big Data in Omics and Imaging: Association Analysis

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both;

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Big Data in Omics and Imaging: Association Analysis

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both;

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Phase Transitions In Machine Learning

, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to;

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Discovery Science

scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge;

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Quality Research in Literacy and Science Education

Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a;

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Quality Research in Literacy and Science Education

Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a;

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Training Systems Using Python Statistical Modeling

, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive;

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Applied Data Science Using PySpark

available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We;

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Applied Data Science Using PySpark

in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also;

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An Introduction to Machine Learning

evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi;

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Support Vector Data Description

This book is based on the basis that machine learningalgorithms and rank based statistical methods are abetter choice to develop a robust;

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Intelligent Computing Applications for COVID-19

offers recent global case studies and discusses how AI and statistical methods initiatives and applications such as Machine Learning Deep;

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Regularization, Optimization, Kernels, and Support Vector Machines

regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non;

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Regularization, Optimization, Kernels, and Support Vector Machines

, statistical learning, and related areas.;

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Financial Signal Processing and Machine Learning

portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures;

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Machine Learning for Speaker Recognition

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition;

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Introduction to Multivariate Analysis

variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description;

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Introduction to Multivariate Analysis

variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both;

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Introduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and;

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