The past decade has seen an explosion of MachIne learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many MachIne learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
標簽： Auto-MachIne-Learning-Methods-Sys tems-Challenges
上傳時間： 2020-06-10
上傳用戶：shancjb
Pattern recognition has its origins in engineering, whereas MachIne learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
標簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時間： 2020-06-10
上傳用戶：shancjb
This book is a general introduction to MachIne learning that can serve as a reference book for researchers and a textbook for students. It covers fundamental modern topics in MachIne learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
標簽： Foundations Learning MachIne 2nd of
上傳時間： 2020-06-10
上傳用戶：shancjb
MachInelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of MachIne learning. This book is about making MachIne learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標簽： interpretable-MachIne-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of MachIne learning meth- ods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of MachIne learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering andTechnology, aims to provide a “snap- shot” of the state of current research at the interface between MachIne learning and healthcare.
標簽： Technologies Healthcare Learning MachIne
上傳時間： 2020-06-10
上傳用戶：shancjb
MachIne learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., MachIne learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of MachIne learning: data, a model, and learning.
上傳時間： 2020-06-10
上傳用戶：shancjb
隨著計算機技術、通信技術的飛速發展和3C(計算機、通信、消費電子)的融合，嵌入式系統已經滲透到各個領域。在32位嵌入式微處理器市場上，基于ARM(Advanced RISC MachIne)內核的微處理器在市場上處于絕對的領導地位，因此追蹤ARM技術的發展趨勢顯得尤為重要。在嵌入式操作系統的選擇上，Linux一直因其內核精簡、代碼開放、易于移植等特點受到廣大嵌入式系統工程師的青睞。另外，嵌入式系統一旦具備網絡接入功能，其信息處理能力更加強大，因此有必要為嵌入式系統構建Web服務器。 本文主要目的是研究基于ARM的嵌入式Linux開發平臺構建，并在此基礎上進行網絡應用程序的開發。 文章深入剖析了ARM9的體系結構，介紹了基于ARM9的S3C2410開發板的特性及資源；闡述了嵌入式操作系統的相關知識及嵌入式Linux移植的基本方法；搭建了移植所需要的開發環境，主要包括在宿主機Linux操作系統下編譯arm-linux交叉編譯工具等；然后詳細闡述了嵌入式Linux開發平臺的構建過程，包括對BootLoader的分析和移植，Linux2.6內核的結構分析、代碼修改以及內核裁減、配置和移植，網卡驅動程序的移植，以及根文件系統的創建。按文中提供的方法和技巧可以很方便的建立一個ARM-Linux開發平臺。 文章最后給出了基于所建平臺的網絡應用，即在上述所建的軟硬件平臺上創建Web服務器Boa，并基于Boa進行應用開發。最終實現了基于Boa嵌入式Web服務器的服務器端表單處理程序，實現了PC機與目標板的動態網頁交互功能，并且，通過PC機IE瀏覽器可以直接控制目標板上的硬件和可執行程序，以實現對目標板的遠程監控功能。
上傳時間： 2013-04-24
上傳用戶：kernaling
·【英文題名】 Search of Double-fed MachIne Variable Speed System Based on DSP 【作者中文名】 沈睿; 【導師】 廖冬初; 【學位授予單位】 湖北工業大學; 【學科專業名稱】 控制理論與控制工程 【學位年度】 2007 【論文級別】 碩士 【網絡出版投稿人】 湖北工業大學 【網絡出版投稿時間】 2008-09-27 【關鍵詞】 雙饋調速
上傳時間： 2013-04-24
上傳用戶：hanwu
虛擬機自省（Virtual MachIne Introspection，VMI）技術充分利用虛擬機管理器的較高權限，可以實現在單獨的虛擬機中部署安全工具對目標虛擬機進行監測，為進行各種安全研究工作提供了很好的解決途徑，從而隨著虛擬化技術的發展成為一種應用趨勢?；跒楦钊氲睦斫夂透玫膽肰MI技術提供參考作用的目的，本文對VMI技術進行了分析研究。采用分析總結的方法，提出了VMI的概念，分析其實現原理和實現方式；詳細地分析總結了VMI技術在不同領域的研究進展，通過對不同研究成果根據實現方式進行交叉分析比較，得出不同研究成果對應的4種實現方式；分析了VMI技術面臨的語義鴻溝問題；最后對VMI技術研究進行總結和展望。
上傳時間： 2014-08-21
上傳用戶：jkhjkh1982
This application note describes a Linear Technology "Half-Flash" A/D converter, the LTC1099, being connected to a 256 element line scan photodiode array. This technology adapts itself to handheld (i.e., low power) bar code readers, as well as high resolution automated MachIne inspection applications..
上傳時間： 2013-11-21
上傳用戶：lchjng
