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Machine Learning for Networking - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Networking - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes the thoroughly refereed proceedings of the 4th International Conference on Machine Learning for Networking, MLN 2021, held in Paris, France, in December 2021. The 10 revised full papers included in the volume were carefully reviewed and selected from 30 submissions. They present and discuss new trends in in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G systems, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, resource allocation, energy-aware communications, software-defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, and underwater sensor networks.

DKK 476.00
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Machine Learning for Networking - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Networking - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

DKK 634.00
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Machine Learning for Networking - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Networking - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

DKK 426.00
1

Machine Learning in Clinical Neuroscience - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning in Clinical Neuroscience - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions first before progressing to more advanced and specific topics, the book is well-suited even for clinicians without prior machine learning experience. Authored by a wide array of experienced global machine learning groups, the book is aimed at clinicians who are interested in mastering the basics of machine learning and who wish to get started with their own machine learning research. The volume is structured in two major parts: The first uniquely introduces all major concepts in clinical machine learning from the ground up, and includes step-by-step instructions on how to correctly develop and validate clinical prediction models. It also includes methodological and conceptual foundations of other applications of machine learning in clinical neuroscience, such as applications of machine learning to neuroimaging, natural language processing, and time series analysis. The second part provides an overview of some state-of-the-art applications of these methodologies. The Machine Intelligence in Clinical Neuroscience (MICN) Laboratory at the Department of Neurosurgery of the University Hospital Zurich studies clinical applications of machine intelligence to improve patient care in clinical neuroscience. The group focuses on diagnostic, prognostic and predictive analytics that aid in decision-making by increasing objectivity and transparency to patients. Other major interests of our group members are in medical imaging, and intraoperative applications of machine vision.

DKK 986.00
1

An Introduction to Machine Learning - Sanjay Churiwala - Bog - Springer Nature Switzerland AG - Plusbog.dk

An Introduction to Machine Learning - Sanjay Churiwala - Bog - Springer Nature Switzerland AG - Plusbog.dk

Just like electricity, Machine Learning will revolutionize our life in many ways - some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. - Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; - Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; - Not tied to any specific software language or hardware implementation.

DKK 986.00
1

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi''an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

DKK 426.00
1

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi''an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

DKK 800.00
1

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi''an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

DKK 967.00
1

Automated Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Automated Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The 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. 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. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.

DKK 434.00
1

The Cultural Life of Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

The Cultural Life of Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book brings together the work of historians and sociologists with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind''s AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents'' capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of "learning" does machine learning truly represent? We empirically address these questions and more to provide a baseline for future research. Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

DKK 697.00
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The Cultural Life of Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

The Cultural Life of Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book brings together the work of historians and sociologists with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind''s AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents'' capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of "learning" does machine learning truly represent? We empirically address these questions and more to provide a baseline for future research. Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

DKK 519.00
1

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning and Principles and Practice of Knowledge Discovery in Databases - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops: Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021) Workshop on Parallel, Distributed and Federated Learning (PDFL 2021) Workshop on Graph Embedding and Mining (GEM 2021) Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021) Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021) Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021) Workshop on Bias and Fairness in AI (BIAS 2021) Workshop on Workshop on Active Inference (IWAI 2021) Workshop on Machine Learning for Cybersecurity (MLCS 2021) Workshop on Machine Learning in Software Engineering (MLiSE 2021) Workshop on MIning Data for financial applications (MIDAS 2021) Sixth Workshop on Data Science for Social Good (SoGood 2021) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021) Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)

DKK 731.00
1

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes the proceedings of the Second International Conference on Machine Learning for Cyber Security, ML4CS 2019, held in Xi''an, China in September 2019. The 23 revised full papers and 3 short papers presented were carefully reviewed and selected from 70 submissions. The papers detail all aspects of machine learning in network infrastructure security, in network security detections and in application software security.

DKK 426.00
1

Big Data, Machine Learning, and Applications - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Big Data, Machine Learning, and Applications - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes refereed proceedings of the First International First International Conference on Big Data, Machine Learning, and Applications, BigDML 2019, held in Silchar, India, in December. The 6 full papers and 3 short papers were carefully reviewed and selected from 152 submissions. The papers present research on such topics as computing methodology; machine learning; artificial intelligence; information systems; security and privacy.

DKK 604.00
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Machine Learning Modeling for IoUT Networks - Ahmad A. Aziz El Banna - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning Modeling for IoUT Networks - Ahmad A. Aziz El Banna - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT). The authors first present seawater''s key physical variables and go on to discuss opportunistic transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition, the authors present applications of machine learning techniques for opportunistic communication and underwater localization. They also discuss the current challenges of machine learning modeling of underwater communication from two communication engineering and data science perspectives.

DKK 434.00
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Deployable Machine Learning for Security Defense - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Deployable Machine Learning for Security Defense - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes selected and extended papers from the Second International Workshop on Deployable Machine Learning for Security Defense, MLHat 2021, held in August 2021. Due to the COVID-19 pandemic the conference was held online. The 6 full papers were thoroughly reviewed and selected from 7 qualified submissions. The papers are organized in topical sections on machine learning for security, and malware attack and defense.

DKK 519.00
1

Machine Learning, Optimization, and Data Science - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning, Optimization, and Data Science - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018. The 46 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

DKK 426.00
1

Machine Learning, Optimization, and Data Science - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning, Optimization, and Data Science - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

DKK 426.00
1

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.

DKK 434.00
1

A Primer on Machine Learning in Subsurface Geosciences - Shuvajit Bhattacharya - Bog - Springer Nature Switzerland AG - Plusbog.dk

A Primer on Machine Learning in Subsurface Geosciences - Shuvajit Bhattacharya - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.

DKK 519.00
1

Artificial Intelligence and Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Artificial Intelligence and Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book contains a selection of the best papers of the 31st Benelux Conference on Artificial Intelligence, BNAIC 2019, and 28th Belgian Dutch Machine Learning Conference, BENELEARN 2019, held in Brussels, Belgium in November 2019. The 11 papers presented in this volume were carefully reviewed and selected from 50 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.

DKK 434.00
1