This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very ...(展开全部)

This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify three reasons for the success of SVMs: their ability to learn well with only a very ...(展开全部)

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tas...(展开全部)

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tas...(展开全部)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established a...(展开全部)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established a...(展开全部)

Control was the word Dr Toby Harper lived by. She strove to keep her life in order, her ER in order. But no one could have been prepared for the man she admits one quiet night to the Springer Hospital. Delirious and in critical condition, he barely responds to treatment. And then he disappears without trace. The subsequent search leads Toby to a second patient with the same inf...(展开全部)

Tess Gerritsen joined the front ranks of thriller writers with her "New York Times" bestseller, "Harvest." Now she weaves utter medical authenticity into another novel of gripping suspense, as a dedicated woman doctor probes too deeply into the cause of a lethal, mystifying outbreak. The overnight ER rotation at Springer Hospital suits Dr. Toby Harper just fine -- until its ca...(展开全部)

Control was the word Dr Toby Harper lived by. She strove to keep her life in order, her ER in order. But no one could have been prepared for the man she admits one quiet night to the Springer Hospital. Delirious and in critical condition, he barely responds to treatment. And then he disappears without trace. The subsequent search leads Toby to a second patient with the same inf...(展开全部)

泰絲．格里森 Tess Gerritsen  《史蒂芬．金談寫作》書單推薦作家，其著作為史蒂芬．金藏書必備  出版界競相邀請推薦的驚悚大師  國內已引進的《別相信任何人》、《最後理論》、《貝塞尼家的姊妹》、《下流祕密俱樂部》等書均不約而同邀請泰絲推薦，其地位可見一斑  作品已譯成四十國語言，全球銷量高達三千萬冊  《出版人週刊》盛讚她為「醫學懸疑天后」（the medical suspense queen） 出生於加州聖地牙哥。母親是第一代華人移民，擁有華裔血統的她從小就喜歡窩在電影院看驚悚片，因而培養出她對黑暗主題的興趣，並反映在她後來撰寫的小說中。 泰絲畢業於名校史丹佛大學，主修人類學。而後繼續深造，最後取得加州大學舊金山分校醫學博士學位，於夏威夷檀香山展開她繁忙的內科醫師生涯。熱愛寫作的她，結婚生子後為了照顧兩個幼兒減少工作量，並開始...(展开全部)