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• Sales Rank: #4253 in eBooks
• Published on: 2015-12-23
• Released on: 2015-12-23
• Format: Kindle eBook

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The western steppelands of Central Eurasia, stretching from the Danube, through the modern Ukraine and southern Russia, to the Caspian, have historically been the meeting ground of Inner Asian pastoral nomads and the agrarian societies of Eastern Europe and the Caucasus. This volume deals, firstly, with the interaction of the nomads with their sedentary neighbours - the Kievan Rus’ state and the medieval polities of Transcaucasia, Georgia in particular - in the period from the 6th century to the advent of the Mongols. Second, it looks at questions of nomadic ethnogenesis (Oghuz, Hungarian, Qipchaq), at the evolution of nomadic political traditions and the heritage of the Turk empire, and at aspects of indigenous nomadic religious traditions together with the impact of foreign religions on the nomads - notably the conversion of the Khazars to Judaism. A number of articles focus on the Qipchaqs, a powerful confederation of complex Inner Asian origins that played a crucial role in the history of Christian Eastern Europe and Transcaucasia and the Muslim world between the 11th and 13th centuries.

• Sales Rank: #5568594 in Books
• Published on: 2003-01-28
• Original language: English
• Dimensions: 9.25" h x 6.50" w x 1.00" l, 1.49 pounds
• Binding: Hardcover
• 384 pages

Review
'Golden's study touches upon the earliest history of Russia, Ukraine, Persia, Ottoman Turkey, and the Caucasus. In a field that has relatively little published, the compilation of these articles into a single volume is a great benefit.' The Russian Review 'This latest addition to Variorum's collection is to be applauded and will further make Peter Golden's contributions to Eurasian studies more readily available to students and scholars of this and related fields.' BSOAS 'This volume of collected essays by Peter B. Golden represents a prestigious addition to Ashgate's Variorum series, and should be welcomed by all historians of medieval Asia and Russia... The articles that relate to specific, and yet broad-ranging historical problems are paragons of erudition and scholarly brilliance... I recommend this book warmly to everyone who takes medieval Eurasian history seriously, and invite readers to reflect on the degree of scholarship required to make even minute progress in this field, which is, not surprisingly, as sparsely inhabited as the steppe region itself. The kind of knowledge that Peter Golden displays in his work cannot be replicated on the basis of today's academic training, and, sadly, one must concede that research of a standard comparable to this collection may well be unattainable in the future.' Central Asiatic Journal 'One of the leading scholars of the pre-Mongol history of the Western Eurasian Steppe and the surrounding areas, [Peter Golden] is the predominant historian and philologist in this field writing in English... Prof. Golden's research is firstly based on a thorough and deep study of virtually all of the relevant sources for the period, stretching from Arabic, Persian, Aramaic and Hebrew, through Old Slavic, Turkic, and Mongolian, to Chinese and other languages. This is buttressed by a systematic and profound reading of modern studies in a myriad of languages. Time and again I am amazed by the extent of the author's reading of Russian language scholarship, which for some of us is unfortunately a closed book... I highly recommend the volume to all those interested in learning more about the background to the Mongol expansion of the thirteenth century and their subsequent period of widespread political control.' Mongolian Studies

About the Author
Peter B. Golden, Rutgers University, USA

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From the reviews of the second edition: ZENTRALBLATT MATH "...written in a concise style. It must be recommended to scientists of statistics, mathematics, physics, and computer science." SHORT BOOK REVIEWS "This interesting book helps a reader to understand the interconnections between various streams in the empirical modeling realm and may be recommended to any reader who feels lost in modern terminology, such as artificial intelligence, neural networks, machine learning etcetera." "The book by Vapnik focuses on how to estimate a function of parameters from empirical data … . The book is concisely written and is intended to be useful to statisticians, computer scientists, mathematicians, and physicists. … This book is very well written at a very high level of abstract thinking and comprehension. The references are up-to-date." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 75 (2), February, 2005) "The aim of the book is to introduce a wide range of readers to the fundamental ideas of statistical learning theory. … Each chapter is supplemented by ‘Reasoning and Comments’ which describe the relations between classical research in mathematical statistics and research in learning theory. … The book is well suited to promote the ideas of statistical learning theory and can be warmly recommended to all who are interested in computer learning problems." (S. Vogel, Metrika, June, 2002)

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Product details

Series: Information Science and Statistics

Hardcover: 314 pages

Publisher: Springer; 2nd edition (November 19, 1999)

Language: English

ISBN-10: 0387987800

ISBN-13: 978-0387987804

Product Dimensions:

6.1 x 0.8 x 9.2 inches

Shipping Weight: 1.2 pounds (View shipping rates and policies)

Average Customer Review:

4.6 out of 5 stars

8 customer reviews

Amazon Best Sellers Rank:

#889,064 in Books (See Top 100 in Books)

If you are studying statistical machine learning, You should have this book.

Wordy, but a good text to get good (personal) insights of Vapnik.A must-have for anyone working on statistical learning.

I am relatively new to statistical learning theory, though with a solid background in supporting theories and a Master's in Engineering. I found the text readable. I appreciate the historical perspective and the development of concepts by the author. I was generally able to grasp Vapnick's theories and explanations, though often after rereading passages many times.Simple examples would significantly aid the readability and understandability of the text - akin to the way we teach our children. We don't describe all the attributes of a rabbit, we point to a picture of a rabbit and say "bunny". After two or three examples of this my children know the abstract concept of a rabbit (without me having to describe a small, four legged creature with long ears, etc. and then answering the inevitable question of "What's four legged creature mean daddy?"). Particularly with a text about learning theory, one would think it would be full of such examples - at least from a pedagogical point of view.Initially, I didn't mind Vapnick's editorializing, but after a while I find it annoying - I'm sure he didn't single-handedly invent the entire field of statistical learning theory, but he sure doesn't miss any opportunities to tell the reader that he believes he has.

happy with purchase

This is a very readable book by an authority on this subject. The book starts with the statistical learning theory, pioneered by the author and co-worker's work, and gradually leads to the path of discovery of support vector machines. An excellent and distinctive property of support vector machines is that they are robust to small data perturbation and have good generalization ability with function complexity being controlled by VC dimension. The treatment of nonlinear kernel classification and regression is given for the first time in the first edition. The 2nd edition includes significant updates including a separate chapter on support vector regression as well as a section on logistic regression using the support vector approach. Most computations involved in this book can be implemented using a quadratic programming package. The connections of support vector machines to traditional statistical modeling such as kernel density and regression and model selection are also discussed. Thus, this book will be an excellent starting point for learning support vector machines.

This book is meant to be a popularization, of sorts, of the material covered in the considerably more formal and detailed treatment, "Statistical Learning Theory." Some of the other reviewers have commented on how Vapnik's subjective perspective is not as evenhanded as they would like. However, I would not have it any other way. I really enjoyed the fact that he has an organic understanding of the field and he expresses his opinions about it in a relatively unvarnished way; it is undeniable that he played a central role in it. Most readers of this kind of thing should be mature enough to deal with the subjectivity that an author must have in talking about the relevance of their own life's work. He is a bit dismissive of work that he believes is either competitive or is derivative/overlapping with his own (as other reviewers pointed out, this includes nearly all of the American work in the 1980's and 90's).The benefits of such subjectivity is a framing of the problems of machine learning in the context of the grand scheme of mathematics/statistics. The book has many insights that would usually be reserved only for lectures. Since it is subjective, it is not PC and he gives his (rather valuable) opinions and insights. I really appreciated that. The connections to philosophical work in induction (Kant, Popper) and the formalization of this into a study of statistical induction was a brilliant section, though it was clear that the argument was more a interpretation for the risk formulation than an encoding of the philosophical texts. You either find that sort of thing interesting or you don't.In summary, a unique portal into understanding Vapnik's extremely insightful point of view on the subject. He has obviously thought very deeply about topics that he's writing about, and it came through.

A good, albeit highly idiosyncratic, guide to Statistical Learning. The highly personal account of the theory is both the strong point and the drawback of the treatise. On one side, Vapnick never loses sight of the big picture, and gives illuminating insights and formulations of the "basic problems" (as he calls them), that are not found in any other book. The lack of proofs and the slightly erratic organization of the topic make for a brisk, enjoyable reading. On the minus side, the choice of the topics is very biased. In this respect, the book is a self-congratulatory tribute by the author to himself: it appears that the foundations of statistical learning were single-handedly laid by him and his collaborators. This is not really the case. Consistency of the Empircal Risk Measure is rather trivial from the viewpoint of a personal trained in asymptotic statistics, and interval estimators for finite data sets are the subject of much advanced statistical literature. Finally, SVMs and neural nets are just a part of the story, and probably not the most interesting.In a nutshell, what Vapnick shows, he shows very well, and is able to provide the "why" of things as no one else. What he doesn't show... you'll have to find somewhere else (the recent Book of Friedman Hastie & Tibs is an excellent starting point).A last remark. The book is rich in grammatical errors and typos. They could have been corrected in the second edition, but do not detract from the book's readability.

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• Sales Rank: #8786834 in Books
• Published on: 1995
• Binding: Hardcover

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