Learning from data : (Record no. 73671)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04980nam a2201237 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 5201503 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712205544.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 101007t20152007njua ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780470140529 |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | paper |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3/1 |
| 100 1# - AUTHOR NAME | |
| Author | Cherkassky, Vladimir S. |
| 245 10 - TITLE STATEMENT | |
| Title | Learning from data : |
| Sub Title | concepts, theory, and methods / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (xviii, 538 pages) : |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Problem statement, classical approaches, and adaptive learning -- Regularization framework -- Statistical learning theory -- Nonlinear optimization strategies -- Methods for data reduction and dimensionality reduction -- Methods for regression -- Classification -- Support vector machines -- Noninductive inference and alternative learning formulations. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | An interdisciplinary framework for learning methodologies--covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied--showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| Subject | Adaptive signal processing. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| Subject | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| Subject | Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| Subject | Fuzzy systems. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Mulier, Filip. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5201503 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Hoboken, New Jersey : |
| -- | IEEE Press : |
| -- | c2007. |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/19/2015. |
| 695 ## - | |
| -- | Adaptation model |
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| -- | Aerospace electronics |
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| -- | Analytical models |
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| -- | Approximation algorithms |
| 695 ## - | |
| -- | Approximation methods |
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| -- | Artificial intelligence |
| 695 ## - | |
| -- | Artificial neural networks |
| 695 ## - | |
| -- | Bibliographies |
| 695 ## - | |
| -- | Biological system modeling |
| 695 ## - | |
| -- | Biology |
| 695 ## - | |
| -- | Books |
| 695 ## - | |
| -- | Boosting |
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| -- | Clustering algorithms |
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| -- | Clustering methods |
| 695 ## - | |
| -- | Complexity theory |
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| -- | Convergence |
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| -- | Data models |
| 695 ## - | |
| -- | Dictionaries |
| 695 ## - | |
| -- | Eigenvalues and eigenfunctions |
| 695 ## - | |
| -- | Encoding |
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| -- | Estimation |
| 695 ## - | |
| -- | Function approximation |
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| -- | Generators |
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| -- | Hafnium |
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| -- | Humans |
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| -- | Hypercubes |
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| -- | Indexes |
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| -- | Iterative methods |
| 695 ## - | |
| -- | Kernel |
| 695 ## - | |
| -- | Learning systems |
| 695 ## - | |
| -- | Linear approximation |
| 695 ## - | |
| -- | Machine learning |
| 695 ## - | |
| -- | Matrix decomposition |
| 695 ## - | |
| -- | Minimization |
| 695 ## - | |
| -- | Newton method |
| 695 ## - | |
| -- | Optimization |
| 695 ## - | |
| -- | Optimization methods |
| 695 ## - | |
| -- | Parameter estimation |
| 695 ## - | |
| -- | Pattern recognition |
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| -- | Polynomials |
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| -- | Predictive models |
| 695 ## - | |
| -- | Principal component analysis |
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| -- | Probabilistic logic |
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| -- | Probability |
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| -- | Prototypes |
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| -- | Risk management |
| 695 ## - | |
| -- | Sections |
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| -- | Singular value decomposition |
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| -- | Statistical learning |
| 695 ## - | |
| -- | Support vector machines |
| 695 ## - | |
| -- | Symmetric matrices |
| 695 ## - | |
| -- | Taxonomy |
| 695 ## - | |
| -- | Training |
| 695 ## - | |
| -- | Training data |
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| -- | Uncertainty |
| 695 ## - | |
| -- | Unsupervised learning |
| 695 ## - | |
| -- | Vector quantization |
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| -- | Vectors |
| 695 ## - | |
| -- | Zinc |
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