Mining Imperfect Data

Mining Imperfect Data PDF Author: Ronald K. Pearson
Publisher:
ISBN: 9781611976267
Category : Data mining
Languages : en
Pages :

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Book Description
"This second edition of Mining Imperfect Data reflects changes in the size and nature of the datasets commonly encountered for analysis, and the evolution of the tools now available for this analysis"--

Mining Imperfect Data

Mining Imperfect Data PDF Author: Ronald K. Pearson
Publisher:
ISBN: 9781611976267
Category : Data mining
Languages : en
Pages :

Get Book Here

Book Description
"This second edition of Mining Imperfect Data reflects changes in the size and nature of the datasets commonly encountered for analysis, and the evolution of the tools now available for this analysis"--

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers PDF Author: Stephen Boyd
Publisher: Now Publishers Inc
ISBN: 160198460X
Category : Computers
Languages : en
Pages : 138

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Book Description
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.

Machine Learning in Complex Networks

Machine Learning in Complex Networks PDF Author: Thiago Christiano Silva
Publisher: Springer
ISBN: 3319172905
Category : Computers
Languages : en
Pages : 345

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Book Description
This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

Mining Imperfect Data

Mining Imperfect Data PDF Author: Ronald K. Pearson
Publisher: SIAM
ISBN: 0898715822
Category : Computers
Languages : en
Pages : 309

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Book Description
This book discusses the problems that can occur in data mining, including their sources, consequences, detection and treatment.

A Compendium of Machine Learning: Symbolic machine learning

A Compendium of Machine Learning: Symbolic machine learning PDF Author: Garry Briscoe
Publisher: Intellect (UK)
ISBN:
Category : Computers
Languages : en
Pages : 386

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Book Description
Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.

Advances in Financial Machine Learning

Advances in Financial Machine Learning PDF Author: Marcos Lopez de Prado
Publisher: John Wiley & Sons
ISBN: 1119482119
Category : Business & Economics
Languages : en
Pages : 395

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Book Description
Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

MICAI 2005: Advances in Artificial Intelligence

MICAI 2005: Advances in Artificial Intelligence PDF Author: Alexander Gelbukh
Publisher: Springer
ISBN: 3540316531
Category : Computers
Languages : en
Pages : 1223

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Book Description
This book constitutes the refereed proceedings of the 4th Mexican International Conference on Artificial Intelligence, MICAI 2005, held in Monterrey, Mexico, in November 2005. The 120 revised full papers presented were carefully reviewed and selected from 423 submissions. The papers are organized in topical sections on knowledge representation and management, logic and constraint programming, uncertainty reasoning, multiagent systems and distributed AI, computer vision and pattern recognition, machine learning and data mining, evolutionary computation and genetic algorithms, neural networks, natural language processing, intelligent interfaces and speech processing, bioinformatics and medical applications, robotics, modeling and intelligent control, and intelligent tutoring systems.

Machine Learning Methods with Noisy, Incomplete or Small Datasets

Machine Learning Methods with Noisy, Incomplete or Small Datasets PDF Author: Jordi Solé-Casals
Publisher: MDPI
ISBN: 3036512888
Category : Mathematics
Languages : en
Pages : 316

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Book Description
Over the past years, businesses have had to tackle the issues caused by numerous forces from political, technological and societal environment. The changes in the global market and increasing uncertainty require us to focus on disruptive innovations and to investigate this phenomenon from different perspectives. The benefits of innovations are related to lower costs, improved efficiency, reduced risk, and better response to the customers’ needs due to new products, services or processes. On the other hand, new business models expose various risks, such as cyber risks, operational risks, regulatory risks, and others. Therefore, we believe that the entrepreneurial behavior and global mindset of decision-makers significantly contribute to the development of innovations, which benefit by closing the prevailing gap between developed and developing countries. Thus, this Special Issue contributes to closing the research gap in the literature by providing a platform for a scientific debate on innovation, internationalization and entrepreneurship, which would facilitate improving the resilience of businesses to future disruptions. Order Your Print Copy

Machine Learning Proceedings 1991

Machine Learning Proceedings 1991 PDF Author: Lawrence A. Birnbaum
Publisher: Morgan Kaufmann
ISBN: 1483298175
Category : Computers
Languages : en
Pages : 682

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Book Description
Machine Learning

Software Engineering with Computational Intelligence

Software Engineering with Computational Intelligence PDF Author: Taghi M. Khoshgoftaar
Publisher: Springer Science & Business Media
ISBN: 1461504295
Category : Computers
Languages : en
Pages : 373

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Book Description
The constantly evolving technological infrastructure of the modem world presents a great challenge of developing software systems with increasing size, complexity, and functionality. The software engineering field has seen changes and innovations to meet these and other continuously growing challenges by developing and implementing useful software engineering methodologies. Among the more recent advances are those made in the context of software portability, formal verification· techniques, software measurement, and software reuse. However, despite the introduction of some important and useful paradigms in the software engineering discipline, their technological transfer on a larger scale has been extremely gradual and limited. For example, many software development organizations may not have a well-defined software assurance team, which can be considered as a key ingredient in the development of a high-quality and dependable software product. Recently, the software engineering field has observed an increased integration or fusion with the computational intelligence (Cl) field, which is comprised of primarily the mature technologies of fuzzy logic, neural networks, genetic algorithms, genetic programming, and rough sets. Hybrid systems that combine two or more of these individual technologies are also categorized under the Cl umbrella. Software engineering is unlike the other well-founded engineering disciplines, primarily due to its human component (designers, developers, testers, etc. ) factor. The highly non-mechanical and intuitive nature of the human factor characterizes many of the problems associated with software engineering, including those observed in development effort estimation, software quality and reliability prediction, software design, and software testing.