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Recent Trends in Learning from Data: Tutorials from the Inns Big Data and Deep Learning Conference (Innsbddl2019)

This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the;

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Challenges in Machine Generation of Analytic Products from Multi-Source Data

, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine;

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Activity Learning

Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data provides an in-depth look at computational approaches to activity;

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Recent Trends in Learning From Data

This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the;

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Machine Learning, Optimization, and Big Data

This book constitutes revised selected papers from the First International Workshop on Machine Learning, Optimization, and Big Data, MOD;

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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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Machine Learning with SAP

Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;

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Learning and Decision-Making from Rank Data

The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference;

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Learning and Decision-Making from Rank Data

The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference;

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Learning and Decision-Making from Rank Data

The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference;

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Intelligent Data Engineering and Automated Learning -- IDEAL 2013

submissions. These papers provided a valuable collection of latest research outcomes in data engineering and automated learning, from methodologies;

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Intelligent Learning Systems

, machine learning is only directed to create software that can learn from past experiences. Machine learning is much closer to the data mining;

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Machine Learning

analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data;

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Deep Learning with Structured Data

identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by;

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Knowledge Discovery from Data Streams

coherent overview ofstate-of-the-art research in learning from data streams.;

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Advances in Knowledge Discovery and Data Mining

related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering;

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Advances in Knowledge Discovery and Data Mining

related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering;

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Advances in Knowledge Discovery and Data Mining

related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering;

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Situating Data Science

Science - its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded;

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Adoption of Data Analytics in Higher Education Learning and Teaching

derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative;

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Machine Learning The New AI

A concise overview of machine learning-computer programs that learn from data-which underlies applications that include recommendation;

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Scala Data Analysis Cookbook

recipes for Scala machine learning * Recipes for every stage of the data analysis process, from reading and collecting data to distributed;

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Intelligent Data Engineering and Automated Learning - IDEAL 2016

submissions. They provide a valuable and timely sample of latest research outcomes in data engineering and automated learning ranging from;

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Human-in-the-Loop Machine Learning

* Real-world use cases from well-known data scientists For software developers and data scientists with some basic Machine Learning;

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Data Science and Productivity Analytics

, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysisdata envelopment;

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Intelligent Data Engineering and Automated Learning - IDEAL 2019

engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data;

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Intelligent Data Engineering and Automated Learning IDEAL 2020

machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges;

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