Mining Massive Data Sets for Security

2008
 |  Computers
Mining Massive Data Sets for Security

Author: Françoise Fogelman-Soulié

Publisher: IOS Press

ISBN: 9781586038984

Category: Computers

Page: 389

View: 575

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The real power for security applications will come from the synergy of academic and commercial research focusing on the specific issue of security. Special constraints apply to this domain, which are not always taken into consideration by academic research, but are critical for successful security applications: large volumes: techniques must be able to handle huge amounts of data and perform 'on-line' computation; scalability: algorithms must have processing times that scale well with ever growing volumes; automation: the analysis process must be automated so that information extraction can 'run on its own'; ease of use: everyday citizens should be able to extract and assess the necessary information; and robustness: systems must be able to cope with data of poor quality (missing or erroneous data). The NATO Advanced Study Institute (ASI) on Mining Massive Data Sets for Security, held in Italy, September 2007, brought together around ninety participants to discuss these issues. This publication includes the most important contributions, but can of course not entirely reflect the lively interactions which allowed the participants to exchange their views and share their experience. The bridge between academic methods and industrial constraints is systematically discussed throughout. This volume will thus serve as a reference book for anyone interested in understanding the techniques for handling very large data sets and how to apply them in conjunction for solving security issues.

Statistik-Workshop für Programmierer : [Einführung in Wahrscheinlichkeit und Statistik ; Statistik verstehen mit Python]

2012
 |  Statistics
Statistik-Workshop für Programmierer : [Einführung in Wahrscheinlichkeit und Statistik ; Statistik verstehen mit Python]

Author: Allen Downey

Publisher: O'Reilly Germany

ISBN: 9783868993424

Category: Statistics

Page: 154

View: 304

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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt Datenanalysen mit Python durchführen können. Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für Wahrscheinlichkeitsrechnung und Statistik: Das Buch führt Sie durch eine vollständige Datenanalyse anhand eines durchgängigen Fallbeispiels -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht.

Generatives Deep Learning

2020
 | 
Generatives Deep Learning

Author: David Foster

Publisher:

ISBN: OCLC:1151051275

Category:

Page: 310

View: 377

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Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren. Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z.B. Generative Adversarial Networks (GANs), Variational Autoencoder (VAEs), Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können.

Mining of Massive Datasets

2014-11-03
 | 
Mining of Massive Datasets

Author: Din Wasem

Publisher: CreateSpace

ISBN: 1503072754

Category:

Page: 156

View: 292

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Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization's storage or compute capacity for accurate and timely decision making . Big data is not a single technology but a combination of old and new technologies that helps companies gain actionable insight. Therefore, big data is the capability to manage a huge volume of disparate data, at the right speed, and within the right time frame to allow real-time analysis and reaction. As we note earlier in this chapter, big data is typically broken down by three characteristics: Volume: How much data Velocity: How fast that data is processed Variety: The various types of data Although it's convenient to simplify big data into the three Vs, it can be misleading and overly simplistic. For example, you may be managing a relatively small amount of very disparate, complex data or you may be processing a huge volume of very simple data. That simple data may be all structured or all unstructured. Even more important is the fourth V: veracity. How accurate is that data in predicting business value? Do the results of a big data analysis actually make sense? Determining relevant data is key to delivering value from massive amounts of data. However, big data is defined less by volume - which is a constantly moving target - than by its ever-increasing variety, velocity, variability and complexity .

Compression Schemes for Mining Large Datasets

2013-11-19
 |  Computers
Compression Schemes for Mining Large Datasets

Author: T. Ravindra Babu

Publisher: Springer Science & Business Media

ISBN: 9781447156079

Category: Computers

Page: 197

View: 782

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This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features: describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.

Active Media Technology

2014-07-14
 |  Computers
Active Media Technology

Author: Dominik Slezak

Publisher: Springer

ISBN: 9783319099125

Category: Computers

Page: 572

View: 755

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This book constitutes the proceedings of the 10th International Conference on Active Media Technology, AMT 2014, held in Warsaw, Poland, in August 2014, held as part of the 2014 Web Intelligence Congress, WIC 2014. The 47 full papers presented were carefully reviewed and selected from numerous submissions. The topics of these papers encompass active computer systems; interactive systems and applications of AMT-based systems; active media machine learning and data mining techniques; AMT for the semantic web; social networks and cognitive foundations.

Solving Large Scale Learning Tasks. Challenges and Algorithms

2016-07-02
 |  Computers
Solving Large Scale Learning Tasks. Challenges and Algorithms

Author: Stefan Michaelis

Publisher: Springer

ISBN: 9783319417066

Category: Computers

Page: 387

View: 373

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In celebration of Prof. Morik's 60th birthday, this Festschrift covers research areas that Prof. Morik worked in and presents various researchers with whom she collaborated. The 23 refereed articles in this Festschrift volume provide challenges and solutions from theoreticians and practitioners on data preprocessing, modeling, learning, and evaluation. Topics include data-mining and machine-learning algorithms, feature selection and feature generation, optimization as well as efficiency of energy and communication.

Multiscale and Multiresolution Methods

2012-12-06
 |  Mathematics
Multiscale and Multiresolution Methods

Author: Timothy J. Barth

Publisher: Springer Science & Business Media

ISBN: 9783642562051

Category: Mathematics

Page: 394

View: 901

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Many computionally challenging problems omnipresent in science and engineering exhibit multiscale phenomena so that the task of computing or even representing all scales of action is computationally very expensive unless the multiscale nature of these problems is exploited in a fundamental way. Some diverse examples of practical interest include the computation of fluid turbulence, structural analysis of composite materials, terabyte data mining, image processing, and a multitude of others. This book consists of both invited and contributed articles which address many facets of efficient multiscale representation and scientific computation from varied viewpoints such as hierarchical data representations, multilevel algorithms, algebraic homogeni- zation, and others. This book should be of particular interest to readers interested in recent and emerging trends in multiscale and multiresolution computation with application to a wide range of practical problems.

Data Mining and Machine Learning Applications

2022-03-02
 |  Computers
Data Mining and Machine Learning Applications

Author: Rohit Raja

Publisher: John Wiley & Sons

ISBN: 9781119791782

Category: Computers

Page: 500

View: 532

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DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Artificial Intelligence in Theory and Practice III

2010-08-26
 |  Computers
Artificial Intelligence in Theory and Practice III

Author: Max Bramer

Publisher: Springer

ISBN: 9783642152863

Category: Computers

Page: 252

View: 344

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The papers in this volume comprise the refereed proceedings of the conference Arti- cial Intelligence in Theory and Practice (IFIP AI 2010), which formed part of the 21st World Computer Congress of IFIP, the International Federation for Information Pr- essing (WCC-2010), in Brisbane, Australia in September 2010. The conference was organized by the IFIP Technical Committee on Artificial Int- ligence (Technical Committee 12) and its Working Group 12.5 (Artificial Intelligence Applications). All papers were reviewed by at least two members of our Program Committee. - nal decisions were made by the Executive Program Committee, which comprised John Debenham (University of Technology, Sydney, Australia), Ilias Maglogiannis (University of Central Greece, Lamia, Greece), Eunika Mercier-Laurent (KIM, France) and myself. The best papers were selected for the conference, either as long papers (maximum 10 pages) or as short papers (maximum 5 pages) and are included in this volume. The international nature of IFIP is amply reflected in the large number of countries represented here. I should like to thank the Conference Chair, Tharam Dillon, for all his efforts and the members of our Program Committee for reviewing papers under a very tight de- line.