Privacy-Aware Knowledge Discovery, 

Автор: Mohamed Medhat Gaber
Название: Scientific Data Mining and Knowledge Discovery
ISBN: 3642426247 ISBN-13(EAN): 9783642426247
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book provides the reader with a complete view of the different tools used in the analysis of data for scientific discovery. The book offers both an overview of the state-of-the-art, and lists areas and open issues for future research and development.

Автор: Vincent S. Tseng; Tu Bao Ho; Zhi-Hua Zhou; Arbee L
Название: Advances in Knowledge Discovery and Data Mining
ISBN: 3319066048 ISBN-13(EAN): 9783319066042
Издательство: Springer
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Цена: 97820.00 T
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Описание: The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, held in Tainan, Taiwan, in May 2014. They cover the general fields of pattern mining; graph and network mining; biomedical data mining; and unstructured data and text mining.

Автор: Sikos Leslie F., Seneviratne Oshani W., McGuinness Deborah L.
Название: Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs
ISBN: 3030676803 ISBN-13(EAN): 9783030676803
Издательство: Springer
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Цена: 130430.00 T
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Описание: The Evolution of Context-Aware RDF Knowledge Graphs.- Data Provenance and Accountability on the Web.- The Right (Provenance) Hammer for the Job: a Comparison of Data Provenance Instrumentation.- Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling.- ProvCaRe: A Large-Scale Semantic Provenance Resource for Scientific Reproducibility.- Graph-Based Natural Language Processing for the Pharmaceutical Industry.

Автор: Shu Wu; Qiang Liu; Liang Wang; Tieniu Tan
Название: Context-Aware Collaborative Prediction
ISBN: 9811053723 ISBN-13(EAN): 9789811053726
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book presents two collaborative prediction approaches based on contextual representation and hierarchical representation, and their applications including context-aware recommendation, latent collaborative retrieval and click-through rate prediction.

Автор: Jian Cao; Xiao Liu; Kaijun Ren
Название: Process-Aware Systems
ISBN: 9811010188 ISBN-13(EAN): 9789811010187
Издательство: Springer
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Цена: 46590.00 T
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Описание: This book constitutes the refereed proceedings of the Second International Workshop on Process-Aware Systems, PAS 2015, held in Hangzhou, China, in October 2015. The four revised full papers and two short papers, presented together with five demo papers were carefully reviewed and selected from 16 submissions.

Автор: Tuan Tran Nguyen
Название: A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources
ISBN: 3658269480 ISBN-13(EAN): 9783658269487
Издательство: Springer
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Цена: 37260.00 T
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Описание: To tackle the challenges of the road estimation task, many works employ a fusion of multiple sources. By that, a commonly made assumption is that the sources always are equally reliable. However, this assumption is inappropriate since each source has certain advantages and drawbacks depending on the operational scenarios. Therefore, Tuan Tran Nguyen proposes a novel concept by incorporating reliabilities into the multi-source fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the author estimates the reliability for each source online using classifiers trained with the sensor measurements, the past performance and the context. Using real data recordings, he shows via experimental results that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion.

Автор: Cellier Peggy, Driessens Kurt
Название: Machine Learning and Knowledge Discovery in Databases: International Workshops of Ecml Pkdd 2019, Wьrzburg, Germany, September 16-20, 2019, Proceeding
ISBN: 3030438864 ISBN-13(EAN): 9783030438869
Издательство: Springer
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Цена: 93160.00 T
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Описание: This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Wurzburg, Germany, in September 2019.

Автор: Mohadeseh Ganji; Lida Rashidi; Benjamin C. M. Fung
Название: Trends and Applications in Knowledge Discovery and Data Mining
ISBN: 3030045021 ISBN-13(EAN): 9783030045029
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2018, held in conjunction with the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, in Melbourne, Australia, in June 2018.The 32 revised papers presented were carefully reviewed and selected from 46 submissions. The workshops affiliated with PAKDD 2018 include: Workshop on Big Data Analytics for Social Computing, BDASC, Australasian Workshop on Machine Learning for Cyber-security, ML4Cyber, Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining, BDM, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI, and Workshop on Data Mining for Energy Modeling and Optimization, DaMEMO.

Автор: Annalisa Appice; Pedro Pereira Rodrigues; V?tor Sa
Название: Machine Learning and Knowledge Discovery in Databases
ISBN: 3319235273 ISBN-13(EAN): 9783319235271
Издательство: Springer
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Цена: 81990.00 T
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Описание: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers.

Автор: Charu C. Aggarwal
Название: Machine Learning for Text
ISBN: 3030088073 ISBN-13(EAN): 9783030088071
Издательство: Springer
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Цена: 46570.00 T
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Описание: Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.

Автор: Chu Wesley W.
Название: Data Mining and Knowledge Discovery for Big Data
ISBN: 3642408362 ISBN-13(EAN): 9783642408366
Издательство: Springer
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Цена: 139310.00 T
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Описание: This book address topics ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

Автор: Wan, Cen
Название: Hierarchical feature selection for knowledge discovery
ISBN: 3319979183 ISBN-13(EAN): 9783319979182
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.