395,63 €
439,59 €
-10% with code: EXTRA
Advances in Independent Component Analysis and Learning Machines
Advances in Independent Component Analysis and Learning Machines
395,63
439,59 €
  • We will send in 10–14 business days.
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algor…
  • Publisher:
  • ISBN-10: 0128028068
  • ISBN-13: 9780128028063
  • Format: 19.1 x 23.6 x 2.8 cm, hardcover
  • Language: English
  • SAVE -10% with code: EXTRA

Advances in Independent Component Analysis and Learning Machines (e-book) (used book) | bookbook.eu

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In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.

Examples of topics which have developed from the advances of ICA, which are covered in the book are:

  • A unifying probabilistic model for PCA and ICA
  • Optimization methods for matrix decompositions
  • Insights into the FastICA algorithm
  • Unsupervised deep learning
  • Machine vision and image retrieval

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395,63
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  • Publisher:
  • ISBN-10: 0128028068
  • ISBN-13: 9780128028063
  • Format: 19.1 x 23.6 x 2.8 cm, hardcover
  • Language: English English

In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining.

Examples of topics which have developed from the advances of ICA, which are covered in the book are:

  • A unifying probabilistic model for PCA and ICA
  • Optimization methods for matrix decompositions
  • Insights into the FastICA algorithm
  • Unsupervised deep learning
  • Machine vision and image retrieval

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