Ebook Info
- Published: 2009
- Number of pages: 1100 pages
- Format: PDF
- File Size: 11.28 MB
- Authors: David G. Stork
Description
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
User’s Reviews
Reviews from Amazon users which were colected at the time this book was published on the website:
⭐Came quickly and is in new condition. Already enjoying learning from it.
⭐I am not sure how this book gets consistently high marks. I am using this text for a graduate level course. While it does a decent job covering most of the topics, it has some glaring flaws.For one the Homework Problems it provides are not really representative of what you’re learning in the text. Almost all of the problems revolve around proofs, as opposed to using the concepts in practice. You can seemingly have a good grasp on the material, yet spend hours trying to solve each of the problems they provide for that particular section. My entire class has complained, and even my professor has admitted that even he isn’t sure sometimes how they expect you to solve some of the problems.Secondly, there are very few example problems demonstrated in the text, so the reader doesn’t really get to see the concepts in action so to speak.Also, there is a typo or error on almost every other page, sometimes even on important formulas.Overall, I’d have to think there are better books out there. If this truly is “the best there is” as some reviewers claim, God help the field of Pattern Recognition.
⭐background: taking a 400 level pattern recognition class as an undergradmy biggest problem with this text is the lack of examples in the chapter and the HORRIBLE end of chapter problemsThe most effective way for me to learn a subject is to do concrete examplesYou are lucky if there are 2 examples within a whole chapterOn top of this the end of chapter problems are extremely confusing. They hardly even relate to the material in the section its from. Most problems are some sort of obscure proof where you have to make assumptions that they fail to mention, and if you don’t you can’t solve the problem.Because of this failure in actually applying the ideas, I am left feeling confused trying to grasp some abstract ideaThe only way I’m getting through the subject is from real example problems given by my professor.Oh and the typos are the icing on the cake
⭐This is a great reference for those who want to become experts in Pattern Recognition methods and apply it in Big Data, Signal analysis, and so on. Much of the currently pop stars authors like Christopher M. Bishop, clearly, spent some time on this book first, before release their own issued on this subject.
⭐I liked this book because it does a great job explaining the concepts and the reasoning behind the mathematical formulae. Other books such as “The Elements of Statistical Learning” toss the Math formulas at you and expect you to figure out the significance or the importance of ’em. The book does not shy away from Math – but does a great job presenting it.
⭐Seems a very good book. Could not tell what the little criticism was about. The author goes out of his way to explain and make it pertinet. Happy reading.
⭐Excellent condition
⭐The book seems to be bearly used at all. Great quality. Arrived on time as well. No complaints at all
⭐This book is a good reference book but it should not be used for self study or even as a university textbooks. First, the author assumes the reader to be familiar with a lot of machine learning terminology. Which is odd because at times the author explains trivial stuff to mind numbing details. Second, the book is seriously short of solved examples which can help illustrate the author’s points. Plots are produced without any explanation on how those were generated and many equations are stated without proof. Third, the end of chapter exercises do not have any answers mentioned. So basically you are on your own completely. There is no feedback on whether you have grasped the topic or solved the problem correctly.In short, use this book as a reference material once you have read something more sensible such as Bishop.
⭐Es un excelente libro, todo su contenido es muy completo y entendible; la calidad del libro es muy buena en cuanto a contenido y diseños de impresión.
⭐
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