| 000 -LEADER |
|---|
| fixed length control field |
03172nam a22002657a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240905100632.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240905b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781119716747 |
| 040 ## - CATALOGING SOURCE |
|---|
| Language of cataloging |
eng |
| Transcribing agency |
ASCOT Library |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
Ref 006.3 W73i 2022 |
| Item number |
04634 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Winters-Hilt, Stephen. |
| Relator code |
author. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Informatics and machine learning from martingales to metaheuristics / |
| Statement of responsibility, etc |
by Stephen Winters-Hilt. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hoboken, New Jersey : |
| Name of publisher, distributor, etc |
John Wiley & Sons, Inc. , |
| Date of publication, distribution, etc |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 566 pages: |
| Other physical details |
b&w illus.; 23 cm. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1 Introduction p.1 -- <br/>2 Probabilistic Reasoning and Bioinformatics p.23 -- <br/>3 Information Entropy and Statistical Measures p.47 -- <br/>4 Ad Hoc, Ab Initio, and Bootstrap Signal Acquisition Methods p.77 -- <br/>5 Text Analytics p.125 -- <br/>6 Analysis of Sequential Data Using HMMs p.155 -- <br/>7 Generalized HMMs (GHMMs): Major Viterbi Variants p.207 -- <br/>8 Neuromanifolds and the Uniqueness of Relative Entropy p.235 -- <br/>9 Neural Net Learning and Loss Bounds Analysis p.253 -- <br/>10 Classification and Clustering p.279 -- <br/>11 Search Metaheuristics p.389 -- <br/>12 Stochastic Sequential Analysis (SSA) p.407 -- <br/>13 Deep Learning Tools – TensorFlow p.433 -- <br/>14 Nanopore Detection – A Case Study p.445 -- <br/>Appendix A: Python and Perl System Programming in Linux p.519 -- <br/>Appendix B: Physics p.529 -- <br/>Appendix C: Math p.531.<br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/><br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"This book provides an interdisciplinary presentation on machine learning, bioinformatics and statistics. This book is an accumulation of lecture notes and interesting research tidbits from over two decades of the author's teaching experience. The chapters in this book can be traversed in different ways for different course offerings. In the classroom, the trend is moving towards hands-on work with running code. Therefore, the author provides lots of sample code to explicitly explain and provide example-based code for various levels of project work. This book is especially useful for professionals entering the rapidly growing Machine Learning field due to its complete presentation of the mathematical underpinnings and extensive examples of programming implementations. Many Machine Learning (ML) textbooks miss a strong intro/basis in terms of information theory. Using mutual information alone, for example, a genome's encoding scheme can be 'cracked' with less than one page of Python code. On the implementation side, many ML professional/reference texts often do not shown how to actually access raw data files and reformat the data into some more usable form. Methods and implementations to do this are described in the proposed text, where most code examples are in Python (some in C/C++, Perl, and Java, as well). Once the data is in hand all sorts of fun analytics and advanced machine learning tools can be brought to bear."-- Provided by publisher |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Bioinformatics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computational biology. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer science. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Electronic data processing. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
| Source of classification or shelving scheme |
|
