An attempt is made in this book to give scientists a detailed working knowledge of the powerful mathematical tools available to aid in data interpretation, especially when con fronted with large data sets incorporating many parameters. A minimal amount of com puter knowledge is necessary for successful applications, and we have tried conscien tiously to provide this in the appropriate sections and references. Scientific data are now being produced at rates not believed possible ten years ago. A major goal in any sci entific investigation should be to obtain a critical evaluation of the data generated in a set of experiments in order to extract whatever useful scientific information may be present. Very often, the large number of measurements present in the data set does not make this an easy task. The goals of this book are thus fourfold. The first is to create a useful reference on the applications of these statistical pattern recognition methods to the sciences. The majority of our discussions center around the fields of chemistry, geology, environmen tal sciences, physics, and the biological and medical sciences. In Chapter IV a section is devoted to each of these fields. Since the applications of pattern recognition tech niques are essentially unlimited, restricted only by the outer limitations of.
Auflage
Softcover reprint of the original 1st ed. 1983
Sprache
Verlagsort
Zielgruppe
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 244 mm
Breite: 170 mm
Dicke: 14 mm
Gewicht
ISBN-13
978-1-4615-9333-1 (9781461593331)
DOI
10.1007/978-1-4615-9331-7
Schweitzer Klassifikation
I Philosophical Considerations and Computer Packages.- I.1. Philosophical Considerations.- I.2. Biomedical Computer Program (BMDP).- I.3. Statistical Package for the Social Sciences (SPSS).- I.4. ARTHUR.- I.5. CLUSTAN.- I.6. SAS.- II Pattern Recognition Approach to Data Analysis.- II.1. Preliminary Data Examination.- II.2. Data Stratification.- II.3. Inter Variable Relationships.- II.4. Unsupervised Learning Techniques.- II.5. Supervised Learning Techniques.- II.6. Variable Reduction.- II.7. Data Manipulations.- III Implementation.- III.1. Typical SPSS Runs.- III.2. Typical ARTHUR Runs.- III.3. Typical BMDP Runs.- III.4. Spss Implementations.- III.5. ARTHUR Implementations.- III.6. BMDP Programs.- IV Natural Science Applications.- Biological Applications.- Medical Applications.- Geological and Earth Science Applications.- Environmental Applications.- Physics Applications.- Chemical Applications.- Summary.- References.- Appendix I. Pattern Recognition Definitions and Reference Books.- Appendix II. The Multivariate Normal Distribution.- Appendix III. Data Base Description.- Appendix IV. Indices of BMDP, SPSS, and ARTHUR Packages.- Appendix V. Programs, Manuals, and Reference Information.- Appendix VI. Summary of Analyses and Program Cross-Reference for Chapter II.- Appendix VII. Nonparametric Statistics.- Appendix VIII. Missing Values.- Appendix IX. Standard Scores And Weightings.- Computer Program Index.
