LEADER 03558nam 22005775 450 001 9910480901103321 005 20200630134423.0 010 $a0-387-21760-6 024 7 $a10.1007/978-0-387-21760-4 035 $a(CKB)2660000000021825 035 $a(SSID)ssj0000870942 035 $a(PQKBManifestationID)11531929 035 $a(PQKBTitleCode)TC0000870942 035 $a(PQKBWorkID)10820702 035 $a(PQKB)11447577 035 $a(DE-He213)978-0-387-21760-4 035 $a(MiAaPQ)EBC3072996 035 $a(EXLCZ)992660000000021825 100 $a20130411d1999 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 13$aAn Atlas of Histology$b[electronic resource] /$fby Shu-Xin Zhang 205 $a1st ed. 1999. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d1999. 215 $a1 online resource (XIX, 426 p. 393 illus.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-387-94954-2 320 $aIncludes bibliographical references and index. 327 $a1. Epithelial Tissue -- 2. Connective Tissue -- 3. Cartilage and Bone -- 4. Blood Cells and Hemopoietic Cells -- 5. Muscular Tissue -- 6. Nervous Tissue and Nervous System -- 7. Circulatory System -- 8. Lymphatic Organs -- 9. Respiratory System -- 10. Digestive System -- 11. Urinary System -- 12. Male Reproductive System -- 13. Female Reproductive System -- 14. Endocrine Organs -- 15. The Integument -- 16. The Eye -- 17. The Ear -- References. 330 $aThe beginning student of histology is frequently confronted by a paradox: diagrams in many books that illustrate human microanatomy in a simplified, cartoon-like manner are easy to understand, but are difficult to relate to actual tissue specimens or photographs. In turn, photographs often fail to show some important features of a given tissue, because no individual specimen can show all of the tissue's salient fea­ tures equally well. This atlas, filled with photo-realistic drawings, was prepared to help bridge the gap between the simplicity of diagrams and the more complex real­ ity of microstructure. All of the figures in this atlas were drawn from histological preparations used by students in my histology classes, at the level of light microscopy. Each drawing is not simply a depiction of an individual histological section, but is also a synthesis of the key structures and features seen in many preparations of similar tissues or organs. The illustrations are representative of the typical features of each tissue and organ. The atlas serves as a compendium of the basic morphological characteristics of human tissue which students should be able to recognize. 606 $aHuman anatomy 606 $aCell biology 606 $aHuman physiology 606 $aAnatomy$3https://scigraph.springernature.com/ontologies/product-market-codes/H12005 606 $aCell Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/L16008 606 $aHuman Physiology$3https://scigraph.springernature.com/ontologies/product-market-codes/B13004 615 0$aHuman anatomy. 615 0$aCell biology. 615 0$aHuman physiology. 615 14$aAnatomy. 615 24$aCell Biology. 615 24$aHuman Physiology. 676 $a611/.018 700 $aZhang$b Shu-Xin$4aut$4http://id.loc.gov/vocabulary/relators/aut$0511311 906 $aBOOK 912 $a9910480901103321 996 $aAtlas of histology$9761994 997 $aUNINA LEADER 05468nam 2200697Ia 450 001 996212456103316 005 20230607221059.0 010 $a0-470-33906-3 010 $a0-470-85477-4 010 $a9786610270101 010 $a1-280-27010-1 010 $a0-470-85478-2 035 $a(CKB)1000000000356169 035 $a(EBL)158121 035 $a(OCoLC)53865202 035 $a(SSID)ssj0000251118 035 $a(PQKBManifestationID)11191515 035 $a(PQKBTitleCode)TC0000251118 035 $a(PQKBWorkID)10247589 035 $a(PQKB)11437128 035 $a(SSID)ssj0000366211 035 $a(PQKBManifestationID)12088486 035 $a(PQKBTitleCode)TC0000366211 035 $a(PQKBWorkID)10417776 035 $a(PQKB)11673966 035 $a(MiAaPQ)EBC158121 035 $a(EXLCZ)991000000000356169 100 $a20020529d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical pattern recognition$b[electronic resource] /$fAndrew R. Webb 205 $a2nd ed. 210 $aWest Sussex, England ;$aNew Jersey $cWiley$dc2002 215 $a1 online resource (516 p.) 300 $aDescription based upon print version of record. 311 $a0-470-84513-9 311 $a0-470-84514-7 320 $aIncludes bibliographical references (p. [459]-490) and index. 327 $aStatistical Pattern Recognition; Contents; Preface; Notation; 1 Introduction to statistical pattern recognition; 1.1 Statistical pattern recognition; 1.1.1 Introduction; 1.1.2 The basic model; 1.2 Stages in a pattern recognition problem; 1.3 Issues; 1.4 Supervised versus unsupervised; 1.5 Approaches to statistical pattern recognition; 1.5.1 Elementary decision theory; 1.5.2 Discriminant functions; 1.6 Multiple regression; 1.7 Outline of book; 1.8 Notes and references; Exercises; 2 Density estimation - parametric; 2.1 Introduction; 2.2 Normal-based models 327 $a2.2.1 Linear and quadratic discriminant functions2.2.2 Regularised discriminant analysis; 2.2.3 Example application study; 2.2.4 Further developments; 2.2.5 Summary; 2.3 Normal mixture models; 2.3.1 Maximum likelihood estimation via EM; 2.3.2 Mixture models for discrimination; 2.3.3 How many components?; 2.3.4 Example application study; 2.3.5 Further developments; 2.3.6 Summary; 2.4 Bayesian estimates; 2.4.1 Bayesian learning methods; 2.4.2 Markov chain Monte Carlo; 2.4.3 Bayesian approaches to discrimination; 2.4.4 Example application study; 2.4.5 Further developments; 2.4.6 Summary 327 $a2.5 Application studies2.6 Summary and discussion; 2.7 Recommendations; 2.8 Notes and references; Exercises; 3 Density estimation - nonparametric; 3.1 Introduction; 3.2 Histogram method; 3.2.1 Data-adaptive histograms; 3.2.2 Independence assumption; 3.2.3 Lancaster models; 3.2.4 Maximum weight dependence trees; 3.2.5 Bayesian networks; 3.2.6 Example application study; 3.2.7 Further developments; 3.2.8 Summary; 3.3 k-nearest-neighbour method; 3.3.1 k-nearest-neighbour decision rule; 3.3.2 Properties of the nearest-neighbour rule; 3.3.3 Algorithms; 3.3.4 Editing techniques 327 $a3.3.5 Choice of distance metric3.3.6 Example application study; 3.3.7 Further developments; 3.3.8 Summary; 3.4 Expansion by basis functions; 3.5 Kernel methods; 3.5.1 Choice of smoothing parameter; 3.5.2 Choice of kernel; 3.5.3 Example application study; 3.5.4 Further developments; 3.5.5 Summary; 3.6 Application studies; 3.7 Summary and discussion; 3.8 Recommendations; 3.9 Notes and references; Exercises; 4 Linear discriminant analysis; 4.1 Introduction; 4.2 Two-class algorithms; 4.2.1 General ideas; 4.2.2 Perceptron criterion; 4.2.3 Fisher's criterion 327 $a4.2.4 Least mean squared error procedures4.2.5 Support vector machines; 4.2.6 Example application study; 4.2.7 Further developments; 4.2.8 Summary; 4.3 Multiclass algorithms; 4.3.1 General ideas; 4.3.2 Error-correction procedure; 4.3.3 Fisher's criterion - linear discriminant analysis; 4.3.4 Least mean squared error procedures; 4.3.5 Optimal scaling; 4.3.6 Regularisation; 4.3.7 Multiclass support vector machines; 4.3.8 Example application study; 4.3.9 Further developments; 4.3.10 Summary; 4.4 Logistic discrimination; 4.4.1 Two-group case; 4.4.2 Maximum likelihood estimation 327 $a4.4.3 Multiclass logistic discrimination 330 $aStatistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive intro 606 $aPattern perception$xStatistical methods 606 $aMathematical statistics 615 0$aPattern perception$xStatistical methods. 615 0$aMathematical statistics. 676 $a006.4 700 $aWebb$b Andrew R$g(Andrew Roy)$0268570 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996212456103316 996 $aStatistical pattern recognition$9678262 997 $aUNISA