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From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data
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Item specifics
- Condition
- ISBN
- 9783642212796
- Subject Area
- Mathematics, Computers, Technology & Engineering
- Publication Name
- From Curve Fitting to Machine Learning : An Illustrative Guide to Scientific Data Analysis and Computational Intelligence
- Publisher
- Springer Berlin / Heidelberg
- Item Length
- 9.3 in
- Subject
- Engineering (General), Intelligence (Ai) & Semantics, Applied
- Publication Year
- 2011
- Series
- Intelligent Systems Reference Library
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Weight
- 66.3 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xv, 465 Pages
About this product
Product Identifiers
Publisher
Springer Berlin / Heidelberg
ISBN-10
3642212794
ISBN-13
9783642212796
eBay Product ID (ePID)
109032775
Product Key Features
Number of Pages
Xv, 465 Pages
Language
English
Publication Name
From Curve Fitting to Machine Learning : An Illustrative Guide to Scientific Data Analysis and Computational Intelligence
Publication Year
2011
Subject
Engineering (General), Intelligence (Ai) & Semantics, Applied
Type
Textbook
Subject Area
Mathematics, Computers, Technology & Engineering
Series
Intelligent Systems Reference Library
Format
Hardcover
Dimensions
Item Weight
66.3 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2011-928739
Reviews
From the reviews: "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results. ... All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code." (Leslie P. Piegl, Zentralblatt MATH, Vol. 1236, 2012)
Dewey Edition
23
Series Volume Number
18
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.3
Table Of Content
Introduction.- Curve Fitting.- Clustering.- Machine Learning.- Discussion.- CIP - Computational Intelligence Packages.
Synopsis
Experimental data analysis is at the core of scientific inquiry, and computers have taken this function to a new level. This volume is an interactive guide to complex modern analytical processes from non-linear curve fitting to clustering and machine learning., The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. These sections may be skipped without affecting the main road but they will open up possibly interesting insights beyond the mere data massage. All topics are completely demonstrated with the aid of the commercial computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source so the detailed code of every method is freely accessible. All examples and applications shown throughout the book may be used and customized by the reader without any restrictions. The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction to these topics. Readers with programming skills may easily port and customize the provided code., The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. These sections may be skipped without affecting the main road but they will open up possibly interesting insights beyond the mere data massage. All topics are completely demonstrated with the aid of the commercial computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source so the detailed code of every method is freely accessible. All examples and applications shown throughout the book may be used and customized by the reader without any restrictions. The target readerships are students of(computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction to these topics. Readers with programming skills may easily port and customize the provided code.
LC Classification Number
Q342
Item description from the seller
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- b***_ (1063)- Feedback left by buyer.Past 6 monthsVerified purchaseNo Communication Required. 15 Day Transatlantic Shipment. New Item in Excellent Condition as Described. A Satisfying Buy from a Reliable Seller.The Politics of Safety: The Black Struggle for Police Accountability in La Guard (#226369370109)
- m***k (40)- Feedback left by buyer.Past monthVerified purchaseBook in great condition as advertised. Book was posted in very sturdy and protective packaging. It arrived more than a week earlier than the ETA. So a fantastic experience overall.
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