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Taschenbuch. Zustand: Neu. Neuware -Advances in technology have enabled the collection of data from scientific observations, simulations, and experiments at an ever-increasing pace. For the scientist and engineer to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated data analysis techniques must be applied to find the useful information in the data. Computational scientific discovery methods can be used to this end: they focus on applying computational methods to automate scientific activities, such as finding laws from observational data. In contrast to mining scientific data, which focuses on building highly predictive models, computational scientific discovery puts a strong emphasis on discovering knowledge represented in formalisms used by scientists and engineers, such as numeric equations and reaction pathways.This state-of-the-art survey provides an introduction to computational approaches to the discovery of scientific knowledge and gives an overview of recent advances in this area, including techniques and applications in environmental and life sciences. The 15 articles presented are partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001. More representative coverage of recent research in computational scientific discovery is achieved by a significant number of additional invited contributions.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 344 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -The three volume proceedings LNAI 10534 ¿ 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.The contributions were organized in topical sections named as follows:Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.Part III: applied data science track; nectar track; and demo track.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 900 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -The three volume proceedings LNAI 10534 ¿ 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.The contributions were organized in topical sections named as follows:Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.Part III: applied data science track; nectar track; and demo track.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 916 pp. Englisch.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Advances in technology have enabled the collection of data from scientific observations, simulations, and experiments at an ever-increasing pace. For the scientist and engineer to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated data analysis techniques must be applied to find the useful information in the data. Computational scientific discovery methods can be used to this end: they focus on applying computational methods to automate scientific activities, such as finding laws from observational data. In contrast to mining scientific data, which focuses on building highly predictive models, computational scientific discovery puts a strong emphasis on discovering knowledge represented in formalisms used by scientists and engineers, such as numeric equations and reaction pathways. This state-of-the-art survey provides an introduction to computational approaches to the discovery of scientific knowledge and gives an overview of recent advances in this area, including techniques and applications in environmental and life sciences. The 15 articles presented are partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001. More representative coverage of recent research in computational scientific discovery is achieved by a significant number of additional invited contributions.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The three volume proceedings LNAI 10534 - 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The three volume proceedings LNAI 10534 - 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.