| 000 -LEADER |
|---|
| fixed length control field |
01811 a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20211220100057.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
211220b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| ISBN |
9781108493703 |
| 040 ## - CATALOGING SOURCE |
|---|
| Transcribing agency |
IIT Kanpur |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
612.8 |
| Item number |
B476d |
| 100 ## - MAIN ENTRY--AUTHOR NAME |
|---|
| Personal name |
Bielza, Concha |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data-driven computational neuroscience |
| Remainder of title |
machine learning and statistical models |
| Statement of responsibility, etc |
Concha Bielza and Pedro Larranaga |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher |
Cambridge University Press |
| Year of publication |
2021 |
| Place of publication |
Cambridge |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Number of Pages |
xviii, 689p |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks, and support vector machines) and probabilistic models (discriminant analysis, logistic regression, and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers, and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical, and behavioral neuroscience levels are considered. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical Term |
Neurosciences -- Data processing |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Larranaga, Pedro |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
