Entropy Randomization in Machine Learning is popular PDF and ePub book, written by Yuri S. Popkov in 2022-08-09, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Entropy Randomization in Machine Learning can be Read Online from any device for your convenience.

Entropy Randomization in Machine Learning Book PDF Summary

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning, Entropy Randomization in Machine Learning considers several applications to binary classification, modelling the dynamics of the Earth’s population, predicting seasonal electric load fluctuations of power supply systems, and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning, researchers and engineers involved in the development of applied machine learning systems, and researchers of forecasting problems in various fields.

Detail Book of Entropy Randomization in Machine Learning PDF

Entropy Randomization in Machine Learning
  • Author : Yuri S. Popkov
  • Release : 09 August 2022
  • Publisher : CRC Press
  • ISBN : 9781000628739
  • Genre : Computers
  • Total Page : 463 pages
  • Language : English
  • PDF File Size : 20,6 Mb

If you're still pondering over how to secure a PDF or EPUB version of the book Entropy Randomization in Machine Learning by Yuri S. Popkov, don't worry! All you have to do is click the 'Get Book' buttons below to kick off your Download or Read Online journey. Just a friendly reminder: we don't upload or host the files ourselves.

Get Book

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning Author : Yuri S. Popkov,Alexey Yu. Popkov,Yuri A. Dubnov
Publisher : CRC Press
File Size : 32,7 Mb
Get Book
Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy rand...

The Art of Randomness

The Art of Randomness Author : Ronald T. Kneusel
Publisher : No Starch Press
File Size : 13,9 Mb
Get Book
Harness the power of randomness (and Python code) to solve real-world problems in fun, hands-on expe...

Machine Learning Animated

Machine Learning  Animated Author : Mark Liu
Publisher : CRC Press
File Size : 40,6 Mb
Get Book
The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also bee...

Transformers for Machine Learning

Transformers for Machine Learning Author : Uday Kamath,Kenneth Graham,Wael Emara
Publisher : CRC Press
File Size : 9,9 Mb
Get Book
Transformers are becoming a core part of many neural network architectures, employed in a wide range...

Deep and Shallow

Deep and Shallow Author : Shlomo Dubnov,Ross Greer
Publisher : CRC Press
File Size : 45,9 Mb
Get Book
Provides a holistic overview of the foundational ideas in music, from the physical and mathematical ...

Neural Information Processing

Neural Information Processing Author : Chu Kiong Loo,Yap Keem Siah,Kok Wai Wong,Andrew Teoh Beng Jin,Kaizhu Huang
Publisher : Springer
File Size : 36,5 Mb
Get Book
The three volume set LNCS 8834, LNCS 8835, and LNCS 8836 constitutes the proceedings of the 20th Int...

Learning Theory

Learning Theory Author : John Shawe-Taylor,Yoram Singer
Publisher : Springer
File Size : 31,7 Mb
Get Book
This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COL...