Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014

Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014 PDF Author: Hoffmann, Frank
Publisher: KIT Scientific Publishing
ISBN: 3731502755
Category :
Languages : en
Pages : 380

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Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014

Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014 PDF Author: Hoffmann, Frank
Publisher: KIT Scientific Publishing
ISBN: 3731502755
Category :
Languages : en
Pages : 380

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Book Description


Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015

Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015 PDF Author: Hoffmann, Frank
Publisher: KIT Scientific Publishing
ISBN: 3731504324
Category :
Languages : en
Pages : 326

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Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016

Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016 PDF Author: Hoffmann, Frank
Publisher: KIT Scientific Publishing
ISBN: 3731505886
Category :
Languages : en
Pages : 294

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Enhancing Surrogate-Based Optimization Through Parallelization

Enhancing Surrogate-Based Optimization Through Parallelization PDF Author: Frederik Rehbach
Publisher: Springer Nature
ISBN: 3031306090
Category : Technology & Engineering
Languages : en
Pages : 123

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Book Description
This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case. Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.

QoS in Wireless Sensor/Actuator Networks and Systems

QoS in Wireless Sensor/Actuator Networks and Systems PDF Author: Mário Alves
Publisher: MDPI
ISBN: 3038973629
Category : Technology & Engineering
Languages : en
Pages : 202

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Book Description
This book is a printed edition of the Special Issue "QoS in Wireless Sensor/Actuator Networks and Systems" that was published in JSAN

Biomedical Natural Language Processing

Biomedical Natural Language Processing PDF Author: Kevin Bretonnel Cohen
Publisher: John Benjamins Publishing Company
ISBN: 9027271062
Category : Computers
Languages : en
Pages : 174

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Book Description
Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the perspective of both biological science and clinical medicine. The intended audience is readers who already have a background in natural language processing, but a clear introduction makes it accessible to readers from the fields of bioinformatics and computational biology, as well. The book is suitable as a reference, as well as a text for advanced courses in biomedical natural language processing and text mining.

Computational Intelligence in Intelligent Data Analysis

Computational Intelligence in Intelligent Data Analysis PDF Author: Christian Moewes
Publisher: Springer
ISBN: 3642323782
Category : Technology & Engineering
Languages : en
Pages : 298

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Book Description
Complex systems and their phenomena are ubiquitous as they can be found in biology, finance, the humanities, management sciences, medicine, physics and similar fields. For many problems in these fields, there are no conventional ways to mathematically or analytically solve them completely at low cost. On the other hand, nature already solved many optimization problems efficiently. Computational intelligence attempts to mimic nature-inspired problem-solving strategies and methods. These strategies can be used to study, model and analyze complex systems such that it becomes feasible to handle them. Key areas of computational intelligence are artificial neural networks, evolutionary computation and fuzzy systems. As only a few researchers in that field, Rudolf Kruse has contributed in many important ways to the understanding, modeling and application of computational intelligence methods. On occasion of his 60th birthday, a collection of original papers of leading researchers in the field of computational intelligence has been collected in this volume.

Synergies of Soft Computing and Statistics for Intelligent Data Analysis

Synergies of Soft Computing and Statistics for Intelligent Data Analysis PDF Author: Rudolf Kruse
Publisher: Springer Science & Business Media
ISBN: 3642330428
Category : Technology & Engineering
Languages : en
Pages : 555

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Book Description
In recent years there has been a growing interest to extend classical methods for data analysis. The aim is to allow a more flexible modeling of phenomena such as uncertainty, imprecision or ignorance. Such extensions of classical probability theory and statistics are useful in many real-life situations, since uncertainties in data are not only present in the form of randomness --- various types of incomplete or subjective information have to be handled. About twelve years ago the idea of strengthening the dialogue between the various research communities in the field of data analysis was born and resulted in the International Conference Series on Soft Methods in Probability and Statistics (SMPS). This book gathers contributions presented at the SMPS'2012 held in Konstanz, Germany. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.

Machine Learning and Data Mining

Machine Learning and Data Mining PDF Author: Ryszad S. Michalski
Publisher: Wiley
ISBN: 9780471971993
Category : Computers
Languages : en
Pages : 472

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Book Description
Master the new computational tools to get the most out of your information system. This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data.

Automated Machine Learning

Automated Machine Learning PDF Author: Frank Hutter
Publisher: Springer
ISBN: 3030053180
Category : Computers
Languages : en
Pages : 223

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Book Description
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.