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Author : Norbert Huber
Publisher : Frontiers Media SA
Page : 235 pages
File Size : 47,38 MB
Release : 2020-04-22
Category :
ISBN : 2889636518
Author : Stefan Sandfeld
Publisher : Springer Nature
Page : 629 pages
File Size : 16,80 MB
Release :
Category :
ISBN : 3031465652
Author : Mohammed J. Zaki
Publisher : Cambridge University Press
Page : 779 pages
File Size : 23,92 MB
Release : 2020-01-30
Category : Business & Economics
ISBN : 1108473989
New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.
Author : Rohit Raja
Publisher : John Wiley & Sons
Page : 500 pages
File Size : 24,76 MB
Release : 2022-01-26
Category : Computers
ISBN : 1119792509
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.
Author : Mohammed J. Zaki
Publisher : Cambridge University Press
Page : 607 pages
File Size : 32,26 MB
Release : 2014-05-12
Category : Computers
ISBN : 0521766338
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Author : Abby L. Parrill
Publisher : John Wiley & Sons
Page : 486 pages
File Size : 43,69 MB
Release : 2016-04-11
Category : Science
ISBN : 1119103932
The Reviews in Computational Chemistry series brings together leading authorities in the field to teach the newcomer and update the expert on topics centered on molecular modeling, such as computer-assisted molecular design (CAMD), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (QSAR). This volume, like those prior to it, features chapters by experts in various fields of computational chemistry. Topics in Volume 29 include: Noncovalent Interactions in Density-Functional Theory Long-Range Inter-Particle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory Efficient Transition-State Modeling using Molecular Mechanics Force Fields for the Everyday Chemist Machine Learning in Materials Science: Recent Progress and Emerging Applications Discovering New Materials via a priori Crystal Structure Prediction Introduction to Maximally Localized Wannier Functions Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding
Author : Olexandr Isayev
Publisher : John Wiley & Sons
Page : 304 pages
File Size : 27,83 MB
Release : 2019-12-04
Category : Technology & Engineering
ISBN : 3527341218
Provides everything readers need to know for applying the power of informatics to materials science There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials. Materials Informatics: Methods, Tools and Applications is presented in two parts?Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others. -Bridges the gap between materials science and informatics -Covers all the known methodologies and applications of materials informatics -Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials -Examines the state-of-the-art software and tools being used today Materials Informatics: Methods, Tools and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics.
Author : Igor Kononenko
Publisher : Horwood Publishing
Page : 484 pages
File Size : 26,18 MB
Release : 2007-04-30
Category : Computers
ISBN : 9781904275213
Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.
Author : Chandrika Kamath
Publisher : Elsevier Inc. Chapters
Page : 30 pages
File Size : 37,57 MB
Release : 2013-07-10
Category : Technology & Engineering
ISBN : 012805932X
Data mining is the process of uncovering patterns, associations, anomalies, and statistically significant structures and events in data. It borrows and builds on ideas from many disciplines, ranging from statistics to machine learning, mathematical optimization, and signal and image processing. Data mining techniques are becoming an integral part of scientific endeavors in many application domains, including astronomy, bioinformatics, chemistry, materials science, climate, fusion, and combustion. In this chapter, we provide a brief introduction to the data mining process and some of the algorithms used in extracting information from scientific data sets.
Author : Claude Sammut
Publisher : Springer Science & Business Media
Page : 1061 pages
File Size : 40,13 MB
Release : 2011-03-28
Category : Computers
ISBN : 0387307680
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.