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Reverse Hypothesis Machine Learning A Practitioner's Perspective

Kulkarni, Parag. author.

http://dx.doi.org/10.1007/978-3-319-55312-2

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Reverse Hypothesis Machine Learning A Practitioner's Perspective / Kulkarni, Parag. author.

Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4CPS 2016

Beyerer, Jürgen. editor.

http://dx.doi.org/10.1007/978-3-662-53806-7

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Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4

Machine Learning Paradigms Artificial Immune Systems and their Applications in Software Personalization

Sotiropoulos, Dionisios N. author.

http://dx.doi.org/10.1007/978-3-319-47194-5

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Machine Learning Paradigms Artificial Immune Systems and their Applications in Software

State-Space Approaches for Modelling and Control in Financial Engineering Systems theory and machine learning methods

Rigatos, Gerasimos G. author.

http://dx.doi.org/10.1007/978-3-319-52866-3

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machine learning methods / Rigatos, Gerasimos G. author.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Duriez, Thomas. author.

http://dx.doi.org/10.1007/978-3-319-40624-4

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Machine Learning Control – Taming Nonlinear Dynamics and Turbulence Duriez, Thomas. author.

Genetic Algorithm Essentials

Kramer, Oliver. author.

http://dx.doi.org/10.1007/978-3-319-52156-5

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-- Multimodality -- Constraints -- Multiple Objectives -- Part III: Advanced Concepts -- Theory -- Machine Learning

Data Privacy: Foundations, New Developments and the Big Data Challenge

Torra, Vicenç. author.

http://dx.doi.org/10.1007/978-3-319-57358-8

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Introduction -- Machine and Statistical Learning -- On the Classiﬁcation of Protection Procedures

Twin Support Vector Machines Models, Extensions and Applications

Jayadeva. author.

http://dx.doi.org/10.1007/978-3-319-46186-1

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, it also discusses the important and challenging applications of this new machine learning methodology

Cover image for Advances in Computational Intelligence Systems Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK

Advances in Computational Intelligence Systems Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK

Angelov, Plamen. editor.

http://dx.doi.org/10.1007/978-3-319-46562-3

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, evolutionary computation, evolving systems and machine learning. The individual chapters are based on peer

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Advances in Systems Science Proceedings of the International Conference on Systems Science 2016 (ICSS 2016)

Świątek, Jerzy. editor.

http://dx.doi.org/10.1007/978-3-319-48944-5

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theory, control theory, machine learning, artificial intelligence, signal processing, communication and

11.

EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII

Emmerich, Michael. editor.

http://dx.doi.org/10.1007/978-3-319-49325-1

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multiobjective optimization, game theory, and machine learning problems. It provides extended versions of

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Artificial Intelligence in Label-free Microscopy Biological Cell Classification by Time Stretch

Mahjoubfar, Ata. author.

http://dx.doi.org/10.1007/978-3-319-51448-2

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-driven diagnosis. • Demonstrates how machine learning is used in high-speed microscopy imaging to facilitate

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