From Curve Fitting to Machine Learning: An Illustrative by Achim Zielesny

By Achim Zielesny

This profitable e-book presents in its moment version an interactive and illustrative advisor from two-dimensional curve becoming to multidimensional clustering and computing device studying with neural networks or aid vector machines. alongside the best way issues like mathematical optimization or evolutionary algorithms are touched. All thoughts and ideas are defined in a transparent reduce demeanour with graphically depicted plausibility arguments and a bit basic mathematics.

The significant themes are commonly defined with exploratory examples and purposes. the first target is to be as illustrative as attainable with no hiding difficulties and pitfalls yet to handle them. the nature of an illustrative cookbook is complemented with particular sections that handle extra primary questions just like the relation among computer studying and human intelligence.

All subject matters are thoroughly established with the computing platform Mathematica and the Computational Intelligence programs (CIP), a high-level functionality library constructed with Mathematica's programming language on most sensible of Mathematica's algorithms. CIP is open-source and the special code used during the publication is freely accessible.

The aim readerships are scholars of (computer) technology and engineering in addition to clinical practitioners in and academia who deserve an illustrative creation. Readers with programming talents may perhaps simply port or customise the supplied code. "'From curve becoming to desktop studying' is ... an invaluable publication. ... It comprises the elemental formulation of curve becoming and similar topics and throws in, what's lacking in such a lot of books, the code to breed the results.
All in all this is often an enticing and worthy e-book either for amateur in addition to professional readers. For the beginner it's a sturdy introductory ebook and the specialist will take pleasure in the numerous examples and dealing code". Leslie A. Piegl (Review of the 1st version, 2012).

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Additional info for From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence

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In practice it is often hard to recognize what went wrong if an optimization failure occurs. And although there are numerous parameters to tune local and global optimization methods for specific optimization problems that does not guarantee to always solve these issues in general. And it becomes clear that any a priori knowledge about the location of an optimum from theoretical considerations or practical experience may play a crucial role. Throughout the later chapters a number of standard problems are discussed and strategies for their circumvention are described.

As a 3D data set those data sets are denoted that contain inputs with two components and outputs with one component: They may be illustrated by three dimensional graphics in contrast to data sets with higher dimensional inputs or outputs. 3 Inputs for clustering Clear["Global‘*"]; The inputs of a data set are defined as the list of inputs of all I/O pairs: input1={Subscript[in,11],Subscript[in,12],Subscript[in,13]}; input2={Subscript[in,21],Subscript[in,22],Subscript[in,23]}; input3={Subscript[in,31],Subscript[in,32],Subscript[in,33]}; inputs={input1,input2,input3} {{in11 , in12 , in13 } , {in21 , in22 , in23 } , {in31 , in32 , in33 }} The inputs data structure may be used for clustering tasks.

G. g. each input may consist of 3 components and each output of 2 components input1={Subscript[in,11],Subscript[in,12],Subscript[in,13]}; output1={Subscript[out,11],Subscript[out,12]}; input2={Subscript[in,21],Subscript[in,22],Subscript[in,23]}; output2={Subscript[out,21],Subscript[out,22]}; input3={Subscript[in,31],Subscript[in,32],Subscript[in,33]}; output3={Subscript[out,31],Subscript[out,32]}; where the first index indicates the I/O pair and the second index the component. The whole data set combines to: dataSet {{{in11 , in12 , in13 } , {out11 , out12 }} , {{in21 , in22 , in23 } , {out21 , out22 }} , {{in31 , in32 , in33 } , {out31 , out32 }}} Data sets do not contain statistical errors since the machine learning methods discussed in this book are not statistically based and therefore do not take errors into account.

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