000 02187cam a22002298i 4500
020 _a9780367720292
020 _a9780367722951
082 0 0 _a5113
_bRIT.I
084 _2Colon Classification
100 1 _aRitter, G. X.,
245 1 0 _aIntroduction to lattice algebra :
_bwith applications in AI, pattern recognition, image analysis, and biomimetic neural networks /
_cGerhard X. Ritter, Gonzalo Urcid.
250 _aFirst edition.
300 _apages cm
504 _aIncludes bibliographical references and index.
505 0 _aElements of algebra -- Pertinent properties of Euclidean space -- Lattice theory -- Lattice algebra -- Matrix-based lattice associative memories -- Extreme points of data sets -- Image unmixing and segmentation -- Lattice-based biomimetic neural networks -- Learning in biomimetic neural networks.
520 _a"Lattice theory extends into virtually every branch of mathematics, ranging from measure theory and convex geometry to probability theory and topology. A more recent development has been the rapid escalation of employing lattice theory for various applications outside the domain of pure mathematics. These applications range from electronic communication theory and gate array devices that implement Boolean logic to artificial intelligence and computer science in general. Introduction to Lattice Theory: With Applications in AI, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks lays emphasis on two subjects, the first being lattice algebra and the second the practical applications of that algebra. This textbook is intended to be used for a special topics course in artificial intelligence with focus on pattern recognition, multispectral image analysis, and biomimetic artificial neural networks. The book is self-contained and - depending on the student's major - can be used at a senior undergraduate level or a first-year graduate level course. The book is also an ideal self-study guide for researchers and professionals in the above-mentioned disciplines"--
650 0 _aLattice theory.
650 0 _aComputer science
650 0 _aArtificial intelligence
700 1 _aUrcid, Gonzalo,
942 _cBK
999 _c655173
_d655173