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Graphical Models for Machine Learning and Digital Communication

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this;

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Learning Probabilistic Graphical Models in R

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R About This Book;

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Pattern Recognition and Machine Learning

distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts;

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Data Analytics in Bioinformatics

becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines;

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Machine Learning

selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and;

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Register Des Graphischen

Starting from the smallest elements of the graphical inventory (grapheme variants, punctuation, and pictographs), this book offers;

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Bayesian and grAphical Models for Biomedical Imaging

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging;

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Computational Methods for Deep Learning

research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book;

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Computational Methods for Deep Learning

research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book;

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Machine Learning in Bioinformatics

discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful;

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Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new;

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Machine Learning with Python for Everyone

The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for;

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Computational Trust Models and Machine Learning

Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various;

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Innovative Psychometric Modeling and Methods

successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from;

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Deploy Machine Learning Models to Production

is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into;

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Machine Learning Modeling for IoUT Networks

transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition;

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Probabilistic Graphical Models

models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several;

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Probabilistic Graphical Models

models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several;

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Digital Signal Processing with Kernel Methods

communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing;

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High-Dimensional Statistics

, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses;

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Machine Learning

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled;

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Introduction to Machine Learning

, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing;

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Applied Machine Learning

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book;

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Innovative Psychometric Modeling and Methods (hc)

successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from;

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OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging

, cognitive models, decision support networks to enhance surgical procedural assistance, context-awareness and team communication in the operating;

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning

submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics;

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Machine Learning with PySpark

embedding for classification. After reading this book, you will understand how to use PySpark's machine learning library to build and train;

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