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Machine learning for data streams : With practical examples in MOA
The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA.
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 and Knowledge Discovery in Databases ; European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II
Constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008.The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer.
Machine Learning and Knowledge Discovery in Databases ; European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I
Constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008.The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer.
Machine Learning and Data Mining for Computer Security : Methods and Applications
Presents research conducted in academia and industry on methods and applications of machine learning and data mining for problems in computer security and will be of interest to researchers and practitioners, as well students.
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.
Learning Theory ; Vol. 4005 ; 19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
Constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA in June 2006. The 43 revised full papers presented together with 2 articles on open problems and 3 invited lectures were carefully reviewed and selected from a total of 102 submissions. The papers cover a wide range of topics including clustering, un- and semisupervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, learning algorithms and limitations on learning, online aggregation, online prediction and reinforcement learning.
Learning from data streams : Processing techniques in sensor networks
The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education.
Large-Scale Knowledge Resources. Construction and Application ; 3rd International Conference on Large-Scale Knowledge Resources, LKR 2008, Tokyo, Japan, March 3-5, 2008. Proceedings
At the start of the 21st century,we are now well on the way to wards aknowled- intensive society, in which knowledge plays ever more important roles. Thus, research interest should inevitably shift from information to knowledge, with the problems of building, organizing, maintaining and utilizing knowledge - coming centralissues in a wide varietyof felds. The 21stCentury COE program “Framework for Systematization and Application of Large-scale Knowledge - sources (COE-LKR)” conducted by the Tokyo Institute of Technology is one of several early attempts worldwide to address these important issues. Inspired by this project, LKR2008 aimed at bringing together diverse contributions in cognitive science, computer science, education and linguistics to explore design, construction, extension, maintenance, validation and application of knowledge.
Languages and Compilers for Parallel Computing ; 20th International Workshop, LCPC 2007, Urbana, IL, USA, October 11-13, 2007, Revised Selected Papers
This book constitutes the thoroughly refereed post-conference proceedings of the 20th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2007, held in Urbana, IL, USA, in October 2007.The 23 revised full papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on reliability, languages, parallel compiler technology, libraries, run-time systems and performance analysis, and general compiler techniques.
Knowledge-Based Intelligent Information and Engineering Systems ; 12th International Conference, KES 2008, Zagreb, Croatia, September 3-5, 2008, Proceedings, Part III
Constitutes the refereed proceedings of the 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008, held in Zagreb, Croatia, in September 2008.The 316 revised papers presented were carefully reviewed and selected. The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the third volume are intelligent data processing in process systems and plants; neural information processing for data mining; soft computing approach to management engineering; advanced groupware; agent and multi-agent systems.
Knowledge discovery, knowledge engineering and knowledge management ; 10th International Joint Conference, IC3K 2018, Seville, Spain, September 18-20, 2018, Revised Selected Papers
Constitutes the thoroughly refereed proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2018, held in Seville, Spain, in September 2018. The 12 full papers presented were carefully reviewed and selected from 167 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering and ontology development; and knowledge management and information sharing.
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 ; 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 2007 ; 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007, Proceedings
The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) was?rstheldin1997inTrondheim, Norway.
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.
KI 2007: Advances in Artificial Intelligence ; 30th Annual German Conference on AI, KI 2007, Osnabrück, Germany, September 10-13, 2007, Proceedings
The 30th Annual German Conference on Artifcial Intelligence (KI-2007) took place in the University of Osnabru ¨ck, September 10–13, 2007. Some topic clusters are apparent in the overall conference program, which re?ect recent trends in AI research, convolved with foci of work in Germany and Europe.
Job Scheduling Strategies for Parallel Processing ; Vol. 3834 : 11th International Workshop, JSSPP 2005, Cambridge, MA, USA, June 19, 2005, Revised Selected Papers
Constitutes the refereed postproceedings of the 11th International Workshop on Job Scheduling Strategies for Parallel Processing, 2005, held in conjunction with the 19th ACM International Conference on Supercomputing. This book covers a range of parallel architectures, from distributed grids, through clusters, to massively-parallel supercomputers.
Job Scheduling Strategies for Parallel Processing ; 13th International Workshop, JSSPP 2007, Seattle, WA, USA, June 17, 2007. Revised Papers
Constitutes the thoroughly refereed post-workshop proceedings of the 13th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2007, held in Seattle, WA, USA, in June 2007, in conjunction with the 21st ACM International Conference on Supercomputing, ICS 2007.The 10 revised full research papers presented went through the process of strict reviewing and subsequent improvement. The papers cover all current issues of job scheduling strategies for parallel processing from the supercomputer-centric viewpoint but also address many nontraditional high-performance computing and parallel environments that cannot or need not access a traditional supercomputer.
