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This is a great novel. I have never seen the mindset of isolation and the depression that follows better portrayed. The style of the piece lends itself to a breathless reading. You don't notice that periods are scarce after a while. It has an exquisite flow to it. All the characters are nicely done. The translation is excellent. I really have nothing negative to say about it.
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For those who wish to venture into the world of telling a story without the book, this is one story that will help you avoid "trouble" as you retell the tale. For those who appreciate the appeal of an illustrated tale, the pictures are just enough to delight, but will allow the reader/listener their imagination at the same time.
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I love this book, it's a wonderful gift to anyone who loves that amazing and magical place known as the Vieux Carre.
Advantage 1: Pattern recognition is a field of many disciplines. It has been studied by statisticians, mathematician, probabilists and engineering and people that call themselves computer scientists specializing in artificial intelligence. The field is old and has a long history but each discipline has developed their own jargon and many times the wheel has been reinvented. The advantage of this book is that these young scientists don't see that awful history. They have learned and mastered their subject in a basically engineering jargon but they include many concepts from statistics and statistical learning theory that are not common to engineering texts. This includes such topics as robust regression, ridge regression and spline estimation. Much of the classical statistical literature is cited. The book contains over 600 references including much of the authors own work.
Disadvantage 1: Because they are young they miss some of the important historical literature and key texts. I found it a little disappointing that the bootstrap which is a statistical tool that has played a major role in discriminant analysis (particularly in the estimation of classification error rates) was completely overlooked. Also although many important texts on pattern recognition, machine learning and discriminant analysis are cited the fine text by McLachlan is overlooked as is the recent relevant text by Hastie, Tibshirani and Friedman.
Advantage 2: This book highlights the work of Vapnik and Chervonenkis and provides nice concise descriptions that one can easily refer to when needed. The mathematics is deep and includes reproducing kernel Hilbert space and many important properties from functional analysis and statistical theory.
Disadvantage 2: The authors are more experienced at writing professional papers than at writing text books. Consequently the book does not flow well and the authors freely admit in their preface that it is best not to read the book in sequential order but rather to take the suggestions in the preface that differ based on the readers background and interest.
Having said all this, for someone like me, who is very knowledgeable about statistical pattern recognition this is a great text for getting me up to speed on an exciting new area that I know very little about. I became curious about it when I started reading Vapnik recently.
I am hoping that a careful reading of this book will give me an intuition about why this approach that incorporates kernel methods can be a powerful tool in pattern recognition and classification.
This book should be a useful reference for anyone interested in this research area. It could be used in an engineering or statistics course in pattern recognition at either the undergraduate or graduate levels depending on what material is covered.
In a recent communication with Bernhard Scholkopf I learned that his book was sent for publication before the Hastie et al. book went to press. So that is the only reason it wasn't referenced. I think that point is worth my mentioning in an editing of this review. Also on reflection I do not think the disadvantages are so great as to remove a star. So it is 5 stars for them.
I can only hope that they will reference the work of McLachlan and Hastie et al. in their future books and research on this subject.
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He begins to suspect the retiring architect does not treat his female companion with as much respect as she deserves. He retreats into his home for a time, trying to get away from the world, in a fit of general agitation and anxiety, but eventually returns to his friends' company, and deepens his friendship with the Persian woman, who seems to be growing apart from her companion. The novel ends with an emotional shock, summarizing the story's happenings, and explaining it in highly dramatic terms.
This novel is unequivocally brilliant. Thomas Bernhard (1931-1989) does not employ a style easy to understand at first, but it is worth every ounce of energy invested. For example, he has written this short novel with no paragraph breaks whatsoever. (The book is 135 pages long, but the type is larger than usual and the pages shorter than usual.)
Bernhard writes in an overflowing, fulsome style, not unlike Samuel Beckett, full of language, full of description, incessant, and captivating. This is exactly his strategy: he is trying to capture the reader by forcing them to expend so much energy following his text, his narrative, his story, and his unusual style, that the final words of the story will hit the reader like a ton of bricks. This is Bernhard's signature, and this novel is a fantastic example.
Any reader should try this novel who is interested in an inventive, experimental novel, but one which does not veer too far from normal story-telling. Berhard's novels, for all their roller-coaster style, are actually quite conventional, and "Yes" is a great introduction to his literary work. His vocabulary is sharp, his characters are well spun, his occasional insights are spectacular, and his stories are intruiguing. This novel is highly recommended for anyone wishing to sharpen their mind, find a new adventure after having enjoyed Beckett's works, or introduce themself to one of the finest writers of the 20th century.