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Advances in Knowledge Discovery and Management
  • Language: en
  • Pages: 207

Advances in Knowledge Discovery and Management

This book is a collection of high scientific novel contributions addressing several of these challenges. These articles are extended versions of a selection of the best papers that were initially presented at the French-speaking conferences EGC’2019held in Metz (France, January 21-25, 2019). These extended versions have been accepted after an additional peer-review process among papers already accepted in long format at the conference. Concerning the conference, the long and short papers selection were also the result of a double blind peer review process among the hundreds of papers initially submitted to each edition of the conference (acceptance rate for long papers is about 25%.

Artificial Neural Networks and Machine Learning - ICANN 2011
  • Language: en
  • Pages: 492

Artificial Neural Networks and Machine Learning - ICANN 2011

This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.

Artificial Neural Networks - ICANN 2010
  • Language: en
  • Pages: 575

Artificial Neural Networks - ICANN 2010

  • Type: Book
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  • Published: 2010-09-13
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  • Publisher: Springer

th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from arou...

Trends and Applications in Knowledge Discovery and Data Mining
  • Language: en
  • Pages: 366

Trends and Applications in Knowledge Discovery and Data Mining

This book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, in Macau, China, in April 2019. The 31 revised papers presented were carefully reviewed and selected from a total of 52 submissions. They stem from the following workshops: · PAISI 2019: 14th Pacific Asia Workshop on Intelligence and Security Informatics · WeL 2019: PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future · LDRC 2019: PAKDD 2019 Workshop on Learning Data Representation for Clustering · BDM 2019: 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining · DLKT 2019: 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer

Advances in Knowledge Discovery and Management
  • Language: en

Advances in Knowledge Discovery and Management

  • Type: Book
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  • Published: 2024-03-18
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  • Publisher: Springer

This book comprises a distinguished collection of cutting-edge scientific contributions. Encompassing a wide range of subjects, it delves into machine learning, data mining, text analysis, data visualization, knowledge management, and more. The included articles are expanded versions of carefully selected top papers that were originally presented at the EGC’2020 conferences held in Paris (France, January 27-31, 2020). It is intended for researchers interested in these fields, including PhD and MSc students, and researchers from public or private laboratories. These extended versions underwent an additional peer-review process, building upon the already accepted long-format papers from the conference. The selection of long and short papers for the conference itself followed a rigorous double-blind peer-review process, evaluating numerous submissions (with a long paper acceptance rate of approximately 25%). For more details about the EGC society, please consult egc.asso.fr."

Neural Information Processing
  • Language: en
  • Pages: 790

Neural Information Processing

The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. The 173 full papers presented were carefully reviewed and selected from 645 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 11953, is organized in topical sections on adversarial networks and learning; convolutional neural networks; deep neural networks; feature learning and representation; human centred computing; human centred computing and medicine; hybrid models; and artificial intelligence and cybersecurity.

Extraction et Gestion des Connaissances
  • Language: fr
  • Pages: 538

Extraction et Gestion des Connaissances

La sélection d'articles publiés dans le présent recueil constitue les actes des 22e Journées Internationales Francophones Extraction et Gestion des Connaissances (EGC 2022) qui se sont déroulées à l'université de Tours du 24 au 28 janvier 2022 sur le campus de Blois. L'objectif de ces journées scientifiques est de rassembler dans un même lieu les chercheurs de disciplines connexes (Bases de Données, Statistiques, Apprentissage, Représentation des Connaissances, Gestion des Connaissances et Intelligence Artificielle) et les qui mettent en oeuvre sur des données réelles des méthodes d'extraction et de gestion des connaissances. Cette conférence est un événement majeur, fédé...

Extraction et Gestion des Connaissances
  • Language: fr
  • Pages: 702

Extraction et Gestion des Connaissances

La sélection d'articles publiés dans le présent recueil constitue les actes des 23e Journées Internationales Francophones Extraction et Gestion des Connaissances (EGC 2023) qui se sont déroulées à l'Université Lumière Lyon 2 du 16 janvier au 20 janvier 2023. L'objectif de ces journées scientifiques est de rassembler dans un même lieu les chercheurs de disciplines connexes (Bases de Données, Statistiques, Apprentissage, Représentation des Connaissances, Gestion des Connaissances et Fouille de Données) et les ingénieurs qui mettent en oeuvre sur des données réelles des méthodes d'extraction et de gestion des connaissances. Cette conférence est un événement majeur fédérat...

Advances in Knowledge Discovery and Management
  • Language: en
  • Pages: 340

Advances in Knowledge Discovery and Management

During the last decade, the French-speaking scientific community developed a very strong research activity in the field of Knowledge Discovery and Management (KDM or EGC for “Extraction et Gestion des Connaissances” in French), which is concerned with, among others, Data Mining, Knowledge Discovery, Business Intelligence, Knowledge Engineering and SemanticWeb. The recent and novel research contributions collected in this book are extended and reworked versions of a selection of the best papers that were originally presented in French at the EGC 2009 Conference held in Strasbourg, France on January 2009. The volume is organized in four parts. Part I includes five papers concerned by various aspects of supervised learning or information retrieval. Part II presents five papers concerned with unsupervised learning issues. Part III includes two papers on data streaming and two on security while in Part IV the last four papers are concerned with ontologies and semantic.

Phase Transitions in Machine Learning
  • Language: en
  • Pages: 401

Phase Transitions in Machine Learning

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.