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3.9 étoiles sur 5
20 commentaires
31 internautes sur 34 ont trouvé ce commentaire utile
4.0 étoiles sur 5
Superb Organization of Ideas!
18 novembre 2006
Par Machine Learner
- Publié sur Amazon.com
Format:Relié|Achat authentifié par Amazon
The topics and concepts in this book are exceptionally well organized. After reading it from cover to cover, I could easily see how all the ideas and concepts fit into place. I have two main criticisms. First, the notation is sometimes non-standard, e.g. the r vector is used to denote the label vector and superscripts are used sometimes as subscripts. Second, the explanations are sometimes too brief. For example, when deriving the solution for Least Squares Regression with Quadratic Discriminants, Vandermode matrices are used but the author fails to identify them as such, or to explain why they are useful. If the author were to write an extra sentence on every other page, the explanations would be perfect!
20 internautes sur 23 ont trouvé ce commentaire utile
4.0 étoiles sur 5
Good one to start
14 décembre 2005
Par Subrat Nanda
- Publié sur Amazon.com
Format:Relié
I would like to congratulate the author on writing this book, which is crisp and covers whole range of topics. What I liked the most is a systematic disucssion on a wide variety of areas in machine learning with a certain degree of details.
But at the same time, I will also say that the book at some places,(for eg the treatment of Multi Dimensional scaling and Linear discriminants analysis,) lacks depth in its derivations. Also if some explanatory examples are put,it would help the reader, who is doing a first time reading, in understanding the concepts.
At the same time, I think the book achieves it's target of introducing to the reader, a whole gamet of techniques, at a fairly reasonable level. The book is no doubt, a nice and one-stop quick reference for many topics, as such. A commendable thing is an up to date errata maintained by the author, with latest editions made. I would recommend the book for a quick introduction to the subject.
But at the same time, I will also say that the book at some places,(for eg the treatment of Multi Dimensional scaling and Linear discriminants analysis,) lacks depth in its derivations. Also if some explanatory examples are put,it would help the reader, who is doing a first time reading, in understanding the concepts.
At the same time, I think the book achieves it's target of introducing to the reader, a whole gamet of techniques, at a fairly reasonable level. The book is no doubt, a nice and one-stop quick reference for many topics, as such. A commendable thing is an up to date errata maintained by the author, with latest editions made. I would recommend the book for a quick introduction to the subject.
8 internautes sur 8 ont trouvé ce commentaire utile
5.0 étoiles sur 5
Great book for Learning Machine Learning
16 octobre 2011
Par H. Haberdar
- Publié sur Amazon.com
Format:Relié|Achat authentifié par Amazon
This book is perfect for both the self-learners that like to learn from scratch and for the ones who need to know crucial details of a method in order to use it as a tool. Compared to 'Pattern Classification by Duda, Hart, and Stork', this book has a good balance between providing equations and explaining the idea behind the method. One thing that I like is that the author usually derives the equations. For example, I used the book to implement Hidden Markov Models algorithm in Java for classification. Especially, if you need a good source to learn Support Vector Machines, 'Chapter 10 Linear Discrimination' and 'Chapter 13 Kernel Machines' are the best of their kinds in the Machine Learning literature. Furthermore, examples shown in the figures are unique and very helpful to understand the topic. The author covers some methods that you usually see in the papers but not in the textbooks. Therefore, the book is also a good survey of Machine Learning techniques. In a nutshell, a great resource for those who want to use Machine Learning Algorithms for classification or regression as a tool and for those who want to implement Machine Learning Algorithms in their applications.
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3.9 étoiles sur 5
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