الصفحة 10
الصفحة 10
Machine learning for cyber-physical systems: selected papers from the international conference ML4CPS 2023
Contains selected papers from the international conference ML4CPS – Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), from 29 to 31 March 2023. Cyber-physical systems are adaptive and learning: they analyze their environment and, based on observations, learn patterns, associations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnostics. Machine learning is the key technology for these developments.
Machine learning for cyber security ; 3rd International Conference, ML4CS 2020, Guangzhou, China, October 8–10, 2020, Proceedings, Part III
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.
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2018
Presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Machine Learning for Audio, Image and Video Analysis : Theory and Applications
The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing, while the third part focuses on applications and shows how techniques are applied in actual problems. The fourth part contains detailed appendices that provide notions about the main mathematical instruments used throughout the text
Machine learning approach for cloud data analytics in IoT
Covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications. Elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
Machine learning and deep learning in medical data analytics and healthcare applications
Introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.
Machine Learning and Data Mining in Pattern Recognition ; 5th International Conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007, Proceedings
MLDM / ICDM Medaillie Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia Gottfried Wilhelm von Leibniz. This was the fifth MLDM in Pattern Recognition event held in Leipzig (www.mldm.de). This meeting from the very first event has focused on aspects of machine learning and data mining in pattern recognition problems.
Machine Learning and Data Mining in Pattern Recognition ; 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings
Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.
Machine learning and big data : Concepts, algorithms, tools and applications
Showcase novel use-cases and applications, present empirical research results from user-centered qualitative and quantitative experiments of these new applications, and facilitate a discussion forum to explore the latest trends in big data and machine learning by providing algorithms which can be trained to perform interdisciplinary techniques such as statistics, linear algebra, and optimization and also create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention
Machine Learning : Modeling Data Locally and Globally
Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications.
Local Pattern Detection ; International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers
Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new field knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the field offers the opportunity to combine the expertise of different fields into a common objective. Moreover, within each field diverse methods have been developed and justified with respect to different quality criteria. We have to investigate how these methods can contributet o solving the problem of KDD. Traditionally, KDD was seeking to end global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to end only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new field of local patterns.
Knowledge-Based Intelligent Information and Engineering Systems ; 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, Proceedings, Part II
The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the second volume are artificial intelligence driven engineering design optimization; biomedical informatics: intelligent information management from nanomedicine to public health; communicative intelligence; computational intelligence for image processing and pattern recognition; computational intelligence in human cancer research; computational intelligence techniques for Web personalization; computational intelligent techniques for bioprocess modelling, monitoring and control; intelligent computing for Grid.
Knowledge Engineering : Practice and Patterns ; 16th International Conference, EKAW 2008, Acitrezza, Italy, September 29 - October 2, 2008. Proceedings
Constitutes the refereed proceedings of the 16th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2008, held in Acitrezza, Sicily, Italy, in September/October 2008.The 17 revised full papers and 15 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 102 submissions. The papers are organized in topical sections on knowledge patterns and knowledge representation, matching ontologies and data integration, natural language, knowledge acquisition and annotations, search, query and interaction, as well as ontologies.
Knowledge Discovery in Inductive Databases ; Vol.3933 ; 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers
The 4th International Workshop on Knowledge Discovery in Inductive Databases (KDID 2005) was held in Porto, Portugal, on October 3, 2005 in conjunction with the 16th European Conference on Machine Learning and the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Ever since the start of the ?eld of data mining, it has been realized that the integration of the database technology into knowledge discovery processes was a crucial issue. This vision has been formalized into the inductive database perspective introduced by T. Imielinski and H. Mannila (CACM 1996, 39(11)). The main idea is to consider knowledge discovery as an extended querying p- cess for which relevant query languages are to be speci?ed.
Knowledge Discovery in Inductive Databases ; Vol.3377 : 3rd International Workshop, KDID 2004, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers
Cnstitutes the thoroughly refereed joint postproceedings of the Third International Workshop on Knowledge Discovery in Inductive Databases, KDID 2004, held in Pisa, Italy in September 2004 in association with ECML/PKDD. Inductive Databases support data mining and the knowledge discovery process in a natural way. In addition to usual data, an inductive database also contains inductive generalizations, like patterns and models extracted from the data. This book presents nine revised full papers selected from 23 submissions during two rounds of reviewing and improvement together with one invited paper. Various current topics in knowledge discovery and data mining in the framework of inductive databases are addressed.
Knowledge Discovery in Inductive Databases ; 5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers
Constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006. The papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
Knowledge discovery in databases : PKDD 2006 ; 10th European Conference on Principles and practice of knowledge discovery in databases, Berlin, Germany, September 18-22, 2006, Proceedings
The European Conference on Principles and Practice of Knowledge Discovery in Databases celebrates its tenth anniversary ; the first PKDD took place in 1997 in Trondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in these areas, the only one that provides a common forum for the two closely related ?elds. In 2006, the 6th collocated ECML/PKDD took place during September 18-22, when the Humboldt-Universität zu Berlin hosted the 17th European Conference on Machine Learning (ECML) and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). The successful model of a hierarchical reviewing process that was introduced last year for the ECML/PKDD 2005 in Porto has been taken over in 2006.
Knowledge Discovery in Databases : PKDD 2005 ; 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings
585 different paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scientific work required a tremendous effort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?ed independent reviews per paper (with very few exceptions)and one additional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the final selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besides the core technical program, ECML and PKDD had 6 invited speakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.
Knowledge Cartography : Software Tools and Mapping Techniques
The authors see mapping software as a set of visual tools for reading and writing in a networked age. In an information ocean, the primary challenge is to find meaningful patterns around which we can weave plausible narratives. Maps of concepts, discussions and arguments make the connections between ideas tangible and disputable.With 17 chapters from the leading researchers and practitioners, the reader will find the current state–of-the-art in the field. Part 1 focuses on educational applications in schools and universities, before Part 2 turns to applications in professional communities.
KI 2008 : Advances in Artificial Intelligence ; 31st Annual German Conference on AI, KI 2008, Kaiserslautern, Germany, September 23-26, 2008. Proceedings
This book constitutes the thoroughly refereed proceedings of the 31th Annual German Conference on Artificial Intelligence, KI 2008, held in Kaiserslautern, Germany, September 2008.The 15 revised full papers presented together with 2 invited contributions and 30 posters were carefully reviewed and selected from 77 submissions. The papers cover important areas such as pattern recognition, multi-agent systems, machine learning, natural language processing, constraint reasoning, knowledge representation and management, planning, and temporal reasoning.
