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  Sugiyama, Masashi Add to Alert Profile  
 
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  1 - 5 of 5 reviews    
  Introduction to statistical machine learning
Sugiyama M.,  Morgan Kaufmann Publishers Inc., San Francisco, CA, 2016. 534 pp. Type: Book, Reviews: (2 of 2)

The huge amount of data resulting from the increase in connected computers, mobile devices, and sensors in diverse domains has facilitated a boom of machine learning in recent years. Machine learning, the topic of this book, plays a central role i...
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Apr 11 2017  
   Introduction to statistical machine learning
Sugiyama M.,  Morgan Kaufmann Publishers Inc., San Francisco, CA, 2016. 534 pp. Type: Book, Reviews: (1 of 2)

Recently, I found myself giving an impromptu book review to someone in the bookshop near to my office. I noticed that a man was browsing through a book on machine learning that I had purchased a few months ago. I won’t mention the name of th...
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Jan 11 2017  
  Multi-parametric solution-path algorithm for instance-weighted support vector machines
Karasuyama M., Harada N., Sugiyama M., Takeuchi I.  Machine Learning 88(3): 297-330, 2012. Type: Article

In a weighted support vector machine (WSVM), each training instance has its own weight. So, for example, in nonstationary data analysis, earlier instances may have less weight than later ones, or in heteroscedastic data modeling, larger weights ar...
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Nov 15 2012  
  Density ratio estimation in machine learning
Sugiyama M., Suzuki T., Kanamori T.,  Cambridge University Press, New York, NY, 2012. 344 pp. Type: Book (978-0-521190-17-6)

It is well known that the man-machine ratio between humans and computers has decreased very significantly in recent decades (and continues to do so). Unlike the early days of computing when there were many designers, maintainers, and users per com...
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Nov 2 2012  
  A unified method for optimizing linear image restoration filters
Sugiyama M., Ogawa H.  Signal Processing 82(11): 1773-1787, 2002. Type: Article

An approach for determining optimal linear image restoration filters is developed by the authors in great detail. A subspace information criterion (SIC), which is an unbiased estimator of the expected squared error with finite samples, is employed...
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Apr 2 2003  

   
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