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

-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants;

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Healthcare Data Analytics and Management

and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas;

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Machine Learning with Health Care Perspective

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional;

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Machine Learning with Health Care Perspective

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional;

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Machine Learning Guide for Oil and Gas Using Python

data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas;

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Solving Large Scale Learning Tasks. Challenges and Algorithms

researchers with whom she collaborated. The 23 refereed articles in this Festschrift volume provide challenges and solutions from;

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Practical Applications of Computational Biology and Bioinformatics, 13th International Conference

of omics data. These, in turn, are creating new challenges for the expanding fields of bioinformatics and computational biology, which seek to;

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Multimodal Behavior Analysis in the Wild

intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis;

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Handbook of Research on Machine Learning Innovations and Trends

higher success in smart data analysis, but it increases the overall probability of technological progression. The Handbook of Research on Machine;

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Uncertainty Modelling and Quality Control for Spatial Data

Data quality in multi-dimensional integration Research challenges on data quality in the integration and analysis of data from multiple;

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Uncertainty Modelling and Quality Control for Spatial Data

Data quality in multi-dimensional integration Research challenges on data quality in the integration and analysis of data from multiple;

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Analysis and Design of Next-Generation Software Architectures

challenges and complexities of moving from a central database legacy architecture to a ledger and networked environment. The challenge is not;

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Analysis and Design of Next-Generation Software Architectures

challenges and complexities of moving from a central database legacy architecture to a ledger and networked environment. The challenge is not;

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Advances in Knowledge Discovery and Data Mining

multi-mediamulti-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal;

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Advances in Knowledge Discovery and Data Mining

multi-mediamulti-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal;

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Tensor Networks for Dimensionality Reduction and Large-scale Optimization

and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely;

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Data-Driven Evolutionary Optimization

available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that;

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Docker for Data Science

Deploy a multi-service data science application across a cloud-based system Who This Book Is For Data scientists, machine learning;

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Generic Drug Product Development

Due to a worldwide need for lower cost drug therapy, use of generic and multi-source drug products have been increasing. To meet;

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Generic Drug Product Development

Due to a worldwide need for lower cost drug therapy, use of generic and multi-source drug products have been increasing. To meet;

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Machine Learning and Data Mining in Pattern Recognition

data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.;

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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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Next Generation of Data Mining

Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation;

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Next Generation of Data Mining

Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation;

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Python Machine Learning -

learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn;

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Intelligent Systems in Big Data, Semantic Web and Machine Learning

based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web;

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Applications of Machine Learning in Wireless Communications

Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data;

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