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Deep Learning Foundations
This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.
Text Mining
This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management.
Machine Learning Foundations
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Database Theory and Application
As future generation information technology (FGIT) becomes specialized and fr- mented, it is easy to lose sight that many topics in FGIT have common threads and, because of this, advances in one discipline may be transmitted to others. Presentation of recent results obtained in different disciplines encourages this interchange for the advancement of FGIT as a whole. Of particular interest are hybrid solutions that c- bine ideas taken from multiple disciplines in order to achieve something more signi- cant than the sum of the individual parts. Through such hybrid philosophy, a new principle can be discovered, which has the propensity to propagate throughout mul- faceted disciplines. FGIT 2009...
Advanced Concepts and Technology II, 1996
Shows 25 proposals from 1996 technology demonstrations selected by Army's Battle Labs. Explains how each equipment fulfills war fighting needs. Names, phone numbers given for entries.
Computational Science and Its Applications - ICCSA 2003
The three-volume set, LNCS 2667, LNCS 2668, and LNCS 2669, constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2003, held in Montreal, Canada, in May 2003.The three volumes present more than 300 papers and span the whole range of computational science from foundational issues in computer science and mathematics to advanced applications in virtually all sciences making use of computational techniques. The proceedings give a unique account of recent results in computational science.
Data Science Concepts and Techniques with Applications
This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared ...
Advances in Neural Networks - ISNN 2007
Annotation The three volume set LNCS 4491/4492/4493 constitutes the refereed proceedings of the 4th International Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. The 262 revised long papers and 192 revised short papers presented were carefully reviewed and selected from a total of 1.975 submissions. The papers are organized in topical sections on neural fuzzy control, neural networks for control applications, adaptive dynamic programming and reinforcement learning, neural networks for nonlinear systems modeling, robotics, stability analysis of neural networks, learning and approximation, data mining and feature extraction, chaos and synchronization, neural fuzzy systems, training and learning algorithms for neural networks, neural network structures, neural networks for pattern recognition, SOMs, ICA/PCA, biomedical applications, feedforward neural networks, recurrent neural networks, neural networks for optimization, support vector machines, fault diagnosis/detection, communications and signal processing, image/video processing, and applications of neural networks.
Computational Science and Its Applications - ICCSA 2008
This two-volume set is assembled following the 2008 International Conference on Computational Science and Its Applications, ICCSA 2008, a premium int- national event held in Perugia, Italy, from June 30 to July 3, 2008. The collection of fully refereed high-quality original works accepted as theme papers for presentation at ICCSA 2008 are published in this LNCS proceedings set. This outstanding collection complements the volume of workshop papers, traditionally published by IEEE Computer Society. The continuous support of computational science researchers has helped ICCSA to become a ?rmly established forum in the area of scienti?c computing and the conference itself become a recurring scien...
