Learning by Scaling

Name: Learning by Scaling
Brand: VDM Verlag Dr. Müller
SKU: 9783639190229
Price: 254.63 RON
Availability: InStock

Learning by Scaling: Classification with Varying Gaussians, Parzen Windows, and Moving Least-square Method

Autor: Daohong Xiang

Limbă:

Legare: Carte broșată

Editura: VDM Verlag Dr. Müller

Disponibilitate: La editor doar la comandă

Expediem în 17-27 zile

254.63 lei

Learning theory is the mathematical foundation of machine learning. In this book we are interested i...

Informații despre carte

Autor

Daohong Xiang

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2009

Pagini

EAN

9783639190229

Enbook ID

06827135

Editura

VDM Verlag Dr. Müller

Greutate

130

Dimensiuni

150 x 220 x 5

Descriere completă

Learning theory is the mathematical foundation of machine learning. In this book we are interested in learning multi-scale structures of data and functions from examples. Our main goal is to give solid mathematical foundations to three families of concrete learning algorithms generated by means of the scaling operator. The main motivation for introducing scaling to learning theory is to learn function features or information with different frequency components when the scaling parameter changes, as done for signal processing or image compression in wavelet analysis. Since the scaling parameter varies, learning algorithms provide richer information but the analysis becomes more involved. In this book, we mainly consider three algorithms including classification with varying Gaussian kernels, Parzen windows and moving least-square methods. The analysis should help shed some light on multi-task learning and sparsity learning from a wavelet point of view. The learning theory issues and mathematical analysis conducted for the learning algorithms addressed in this book should be useful and helpful to students and researchers interested in learning theory.

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Learning by Scaling

Learning by Scaling: Classification with Varying Gaussians, Parzen Windows, and Moving Least-square Method

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Descriere completă

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