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Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Le) Paperback – June 29, 2009

ISBN-13: 978-1598295474 ISBN-10: 1598295470
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Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Le) + Semi-Supervised Learning (Adaptive Computation and Machine Learning series)
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Product Details

  • Series: Synthesis Lectures on Artificial Intelligence and Machine Le
  • Paperback: 130 pages
  • Publisher: Morgan and Claypool Publishers (June 29, 2009)
  • Language: English
  • ISBN-10: 1598295470
  • ISBN-13: 978-1598295474
  • Product Dimensions: 7.5 x 0.3 x 9.2 inches
  • Shipping Weight: 9.9 ounces (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #1,102,350 in Books (See Top 100 in Books)

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4 of 4 people found the following review helpful By Shibamouli Lahiri on April 30, 2010
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For a couple of years I have been looking for a book on semi-supervised learning that presents the basic ideas without confusing the reader with deep theory or hard-to-digest algorithms. This book meets my requirement.

Top 10 reasons why I loved this book:

1> Simple language and explanation
2> Natural flow of ideas
3> Organization of material
4> Presentation of material in a form suitable for everyone with modest background in linear algebra
5> Use of illustrative pictures without abusing them (i.e. using too many or too few or using inappropriately)
6> Clarification of difficult concepts with simple, intuitive examples
7> Ability to retain interest of bad readers like me so that I absolutely had no other option but to finish the book in one go
8> Highlighting important facts
9> Print quality
10> The authors' knowledge of how much to present in each chapter and where to stop or draw a line

Although the book is slim, I think my investment was invaluable. Highly recommended for practitioners and beginners alike!
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2 of 2 people found the following review helpful By Vladislavs Dovgalecs on July 10, 2011
Initially I was sceptic about its length knowing that this topic in Machine Learning is complex and broad. Methods are very diverse and it may be hard to gather all of them together and describe them.
Author managed to write about most popular SSL methods like Mixture Models, Co-Training, Graph Methods, SVMs and others in a brief but very clear language. Provided examples and assumptions inherent to each of them.
At the end of each chapter, a rich bibliography is given.
Five stars without hesitation mainly because of short but very clear and precise discussion!
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I am a first year PhD student in Statistics and this book was a perfect introduction to semi-supervised learning. I was looking for a less technical introduction that emphasized ideas rather than mathematical intricacies and this book was a good fit. The authors make it a point to introduce several different approaches to SSL, then give examples where each approach fails, demonstrating the limitations of each method. I found this a clever and excellent approach to getting their ideas across.

Though the book is short and therefore necessarily limited in scope, it gives the reader a general idea about the field of SSL and is a good stepping stone into more technical material. There are plenty of references to keep you busy.
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I wish most technical writers/academic researchers could explain technical concepts like what Zhu does. I have read some of his papers in addition to this book and his writing style is outstanding. It tends to start with some basic concepts with clear graphs and even for deeper technical materials, Zhu seemed to be able to explain very clearly.
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this book provides an intuitive and schematic view of more popular SSL algorithm like co-training and EM based algorithms, it's rich of examples and make this abstract subject quite easy to understand in it's practical applications
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Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Le)
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