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Statistics and Machine Learning Methods for EHR Data

methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data.;

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Statistics and Machine Learning Methods for EHR Data

covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and;

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Data Analytics, Computational Statistics, and Operations Research for Engineers

, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications;

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Recent Methods from Statistics and Machine Learning for Credit Scoring

Recent Methods from Statistics and Machine Learning for Credit Scoring is een boek van Cuvillier Verlag;

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Machine Learning Research Progress

methods extract rules and patterns out of massive data sets. The major focus of machine learning research is to extract information from data;

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Data Mining and Machine Learning

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze;

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Intelligent Learning Systems

neural network architecture for on-line learning, and resampling methods for unsupervised learning from sample data. Section 3 focuses on;

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Information-Theoretic Methods in Data Science

-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad;

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Machine Learning

Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive;

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Principles and Theory for Data Mining and Machine Learning

Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics;

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Introduction to Statistical Machine Learning

machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas;

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Machine Learning For Hackers

is within the reach any experienced programmer willing to crunch data. With this book, you'll learn machine learning and statistics tools in a;

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Big Data in Omics and Imaging, Two Volume Set

models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway;

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Rank-Based Methods for Shrinkage and Selection

Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset;

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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next;

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Efficacy Analysis in Clinical Trials an Update

Machine learning and big data is hot. It is, however, virtually unused in clinical trials. This is so, because randomization is applied to;

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Introduction to Machine Learning

, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and;

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Introduction to Machine Learning

, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and;

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Essential Statistics for Non-STEM Data Analysts

, regression, tree-based methods, and ensemble learning. By the end of this Essential Statistics for Non-STEM Data Analysts book, you'll have;

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High-Dimensional Statistics

. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained;

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Mathematics for Machine Learning

, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data;

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Mathematics for Machine Learning

, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data;

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The Mathematics of Data

interdisciplinary foundations of machine learning and data science.;

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Data Mining and Machine Learning in Cybersecurity

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role;

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Applied Directional Statistics

This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more;

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Practical Statistics for Data Scientists

belongs to Statistical machine learning methods that learn from data Unsupervised learning methods for extracting meaning from unlabeled data;

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Behavior Analysis with Machine Learning Using R

ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning;

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