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Books in Computer science

The Computing collection presents a range of foundational and applied content across computer and data science, including fields such as Artificial Intelligence; Computational Modelling; Computer Networks, Computer Organization & Architecture, Computer Vision & Pattern Recognition, Data Management; Embedded Systems & Computer Engineering; HCI/User Interface Design; Information Security; Machine Learning; Network Security; Software Engineering.

BooksJournals

    • The Art of Technical Documentation

      • 1st Edition
      • May 16, 2014
      • Katherine Haramundanis
      • English
      • Paperback
        9 7 8 1 5 5 5 5 8 0 8 0 3
      • eBook
        9 7 8 1 4 8 3 1 8 4 0 1 2
      The Art of Technical Documentation presents concepts, techniques, and practices in order to produce effective technical documentation. The book provides the definition of technical documentation; qualities of a good technical documentation; career paths and documentation management styles; precepts of technical documentation; practices for gathering information, understanding what you have gathered, and methods for testing documentation; and considerations of information representation, to provide insights on how different representations affect reader perception of your documents. Technical writers and scientists will find the book a good reference material.

    • Machine Learning Proceedings 1995

      • 1st Edition
      • June 28, 2014
      • Armand Prieditis + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 3 7 7 6
      • eBook
        9 7 8 1 4 8 3 2 9 8 6 6 5
      Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning (ML95), held at the Granlibakken Resort in Tahoe City, California on July 9-12, 1995. The book focuses on the processes, methodologies, principles, and approaches involved in machine learning, including inductive logic programming algorithms, neural networks, and decision trees. The selection first offers information on the theory and applications of agnostic PAC-learning with small decision trees; reinforcement learning with function approximation; and inductive learning of reactive action models. Discussions focus on inductive logic programming algorithm, collecting instances for learning, residual gradient algorithms, direct algorithms, and learning curves for decision trees of small depth. The text then elaborates on visualizing high-dimensional structure with the incremental grid growing neural network; empirical support for winnow and weighted-majority based algorithms; and automatic selection of split criterion during tree growing based on node location. The manuscript takes a look at learning hierarchies from ambiguous natural language data, learning with rare cases and small disjuncts, learning by observation and practice, and learning collection fusion strategies for information retrieval. The selection is a valuable source of data for mathematicians and researchers interested in machine learning.

    • Machine Learning Proceedings 1990

      • 1st Edition
      • May 23, 2014
      • Bruce Porter + 1 more
      • English
      • Paperback
        9 7 8 1 5 5 8 6 0 1 4 1 3
      • eBook
        9 7 8 1 4 8 3 2 9 8 5 8 0
      Machine Learning: Proceedings of the Seventh International Conference (1990) covers the research results from 12 disciplines of machine learning represented at the Seventh International Conference on Machine Learning, held on June 21-23, 1990 at the University of Texas in Austin. The book focuses on the progress in the interest in machine learning, including methodologies, approaches, and techniques. The selection first offers information on knowledge acquisition from examples using maximal representation learning, performance analysis of a probabilistic inductive learning system, and a comparative study of ID3 and backpropagation for English text-to-speech mapping. The text then examines learning from data with bounded inconsistency, improving fit-and-split algorithms, and an incremental method for finding multivariate splits for decision trees. Topics include issues for decision-tree induction, learning and approximation, conceptual-set-cover... algorithm, bounded inconsistency, implementation, and examples of incremental processes. The publication ponders on incremental induction of topologically minimal trees, rational analysis of categorization, search control, utility, and concept induction, graph clustering and model learning by data compression, and an analysis of representation shift in concept learning. Learning procedures by environment-driven constructive induction and improving the performance of genetic algorithms in automated discovery of parameters are also discussed. The selection is a valuable source of data for researchers interested in machine learning.

    • Computer-Aided Architectural Design Futures

      • 1st Edition
      • May 20, 2014
      • Alan Pipes
      • English
      • eBook
        9 7 8 1 4 8 3 1 6 2 2 7 0
      Computer-Aided Architectural Design Futures contains the proceeding of the International Conference on Computer-Aided Architectural Design, held at Department of Architecture, Technical University of Delft, The Netherlands on September 18-19, 1985. Organized into four parts, the book underlines concepts on computer-aided architectural design. These include systematic design; drawing and visualization; artificial intelligence and knowledge engineering; and implications for practice. This book will be a major reference text for students, researchers, and practitioners.

    • Campus Strategies for Libraries and Electronic Information

      • 1st Edition
      • June 28, 2014
      • Caroline Arms
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 5 8 0 3
      • eBook
        9 7 8 1 4 8 3 2 9 4 4 8 3
      A look at how ten American colleges and Universities bridged the gap between computing, administrative, and library organisationsDetaile... case studies from ten American colleges and universities will prepare you to make better plans and decisions for an electronic library, integrated information management system, or unified information resource. You'll find models and guidelines covering reference services, latest philosophies and strategies, management and organization issues, delivery mechanisms, and more.

    • Archives and the Computer

      • 1st Edition
      • May 20, 2014
      • Michael J. Cook
      • English
      • Paperback
        9 7 8 1 4 8 3 1 0 7 4 3 1
      • eBook
        9 7 8 1 4 8 3 1 0 1 0 9 5
      Archives and the Computer deals with the use of the computer and its systems and programs in archiving data and other related materials. The book covers topics such as the scope of automated systems in archives; systems for records management, archival description, and retrieval; and machine-readable archives. The book also features examples of systems for records management from different institutions such as theTyne and Wear Archive Department, Dyfed Record Office, and the University of Liverpool. Included in the last part are appendices. Appendix A is a directory of archival systems, Appendix B contains guidelines for machine-readable and related records for preservation, and Appendix C covers machine-readable archives. The text is recommended for archivists who would like to know more about the use of computers in archiving of records and other related information.

    • Pattern Recognition and Artificial Intelligence, Towards an Integration

      • 1st Edition
      • Volume 7
      • June 28, 2014
      • L.N. Kanal + 1 more
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 5 6 5 0
      • eBook
        9 7 8 1 4 8 3 2 9 9 4 5 7
      This volume brings together the results of research into the methodology and applications of pattern recognition, with particular emphasis given to the incorporation of artificial intelligence methodologies into pattern recognition systems.The first part of this volume covers image analysis and processing software, systems and algorithms. Pattern analysis and classifier design are dealt with in part two, while the last part deals with model based and expert systems, including uncertainty calculus methods in pattern analysis and object recognition. A number of specific application areas are considered, including such diverse topics as fingerprinting, astronomy, molecular biology and pathology.

    • Computer Hardware Description Languages and their Applications

      • 1st Edition
      • Volume 32
      • May 21, 2014
      • D. Agnew + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 4 8 1 6 4 1 2
      • eBook
        9 7 8 1 4 8 3 2 9 8 0 2 3
      Hardware description languages (HDLs) have established themselves as one of the principal means of designing electronic systems. The interest in and usage of HDLs continues to spread rapidly, driven by the increasing complexity of systems, the growth of HDL-driven synthesis, the research on formal design methods and many other related advances.This research-oriented publication aims to make a strong contribution to further developments in the field. The following topics are explored in depth: BDD-based system design and analysis; system level formal verification; formal reasoning on hardware; languages for protocol specification; VHDL; HDL-based design methods; high level synthesis; and text/graphical HDLs. There are short papers covering advanced design capture and recent work in high level synthesis and formal verification. In addition, several invited presentations on key issues discuss and summarize recent advances in real time system design, automatic verification of sequential circuits and languages for protocol specification.

    • Artificial Neural Networks and Statistical Pattern Recognition

      • 1st Edition
      • Volume 11
      • June 28, 2014
      • I.K. Sethi + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 4 8 8 7 4 1 2
      • eBook
        9 7 8 1 4 8 3 2 9 7 8 7 3
      With the growing complexity of pattern recognition related problems being solved using Artificial Neural Networks, many ANN researchers are grappling with design issues such as the size of the network, the number of training patterns, and performance assessment and bounds. These researchers are continually rediscovering that many learning procedures lack the scaling property; the procedures simply fail, or yield unsatisfactory results when applied to problems of bigger size. Phenomena like these are very familiar to researchers in statistical pattern recognition (SPR), where the curse of dimensionality is a well-known dilemma. Issues related to the training and test sample sizes, feature space dimensionality, and the discriminatory power of different classifier types have all been extensively studied in the SPR literature. It appears however that many ANN researchers looking at pattern recognition problems are not aware of the ties between their field and SPR, and are therefore unable to successfully exploit work that has already been done in SPR. Similarly, many pattern recognition and computer vision researchers do not realize the potential of the ANN approach to solve problems such as feature extraction, segmentation, and object recognition. The present volume is designed as a contribution to the greater interaction between the ANN and SPR research communities.

    • Progress in Pattern Recognition 1

      • 1st Edition
      • June 28, 2014
      • L.N. Kanal + 1 more
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 5 4 8 3
      • eBook
        9 7 8 1 4 8 3 2 9 5 8 9 3
      Progress in Pattern Recognition, Volume 1 focuses on the processes, techniques, and approaches involved in pattern recognition, including conceptual clustering, cross-correlation, syntax, software, data structures, and distance transforms. The selection first tackles progress in syntactic pattern recognition and clustering objects into classes characterized by conjunctive concepts. Discussions focus on an overview of clustering problems, conjunctive conceptual clustering, primitive selection and pattern grammars, high dimensional grammars for pattern description, syntactic pattern recognition using stochastic languages, and syntactic approach to shape and texture analysis. The text then elaborates on database representations in hierarchical scene analysis and medium level vision. The book examines image processing software and analysis and synthesis of image patterns by spatial interaction models. Topics include synopsis of discrete spatial interaction models, nonrecursive models over infinite lattices, finite lattice models, and the MSFC image processing package. The text also reviews the mathematical aspects of image reconstruction from projection and recognition of stereo-image cross-correlation errors. The selection is a highly recommended source of data for researchers interested in the process of pattern recognition.

Related subjects

Applications

Artificial intelligence

Business intelligence

Computer systems organization

Computer vision and pattern recognition

Computing milieux

Data

Graphics cad hci

Hardware and architecture

Information systems

Internet and web technology

Software

Theory and mathematics