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Statistical and Machine Learning Approaches for Network Analysis

weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Neural Networks and Statistical Learning

, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with;

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Understanding Machine Learning

learning include decision tree learning, artificial neural network learning algorithm, deep learning and association rule learning, besides others;

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Training Systems Using Python Statistical Modeling

Python's rich suite of libraries for statistical modeling Implement regression, clustering and train neural networks from scratch Includes real;

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Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis;

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Computational Reconstruction of Missing Data in Biological Research

analysis, including deep recurrent neural network recovery for feature missings, robust information theoretic learning for label missings and;

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Source Separation and Machine Learning

algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a;

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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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Statistical Reinforcement Learning

field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms;

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Statistical Reinforcement Learning

field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms;

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Machine Learning Theoretical Foundations and Practical Applications

in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;

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Statistics and Machine Learning Methods for EHR Data

The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning;

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Statistical Learning and Data Science

analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and;

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Statistical Learning and Data Science

analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and;

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Statistics and Machine Learning Methods for EHR Data

approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting;

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Statistical Machine Learning for Human Behaviour Analysis

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

future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management;

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Developing Enterprise Chatbots: Learning Linguistic Structures

reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot;

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Big Data in Omics and Imaging, Two Volume Set

analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching;

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Probabilistic Foundations of Statistical Network Analysis

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major;

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Probabilistic Foundations of Statistical Network Analysis

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major;

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Intelligent Learning Systems

for pattern classification. Section 2 focuses on intelligent learning methods and approaches, describing evolutionary learning of concepts;

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Pattern Recognition And Big Data

describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the;

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Social sciences via network analysis and computation

in network analysis, machine learning, data mining, data clustering and classifications in the contents of social sciences. It also covers various;

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Sentiment Analysis in Social Networks

learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to;

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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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