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.
标签： Auto-machIne-Learning-Methods-Sys tems-Challenges
上传时间： 2020-06-10
上传用户：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.
标签： Bishop-Pattern-Recognition-and-Ma chine-Learning
上传时间： 2020-06-10
上传用户：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.
标签： Foundations Learning machIne 2nd of
上传时间： 2020-06-10
上传用户：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.
标签： interpretable-machIne-learning
上传时间： 2020-06-10
上传用户：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.
标签： Technologies Healthcare Learning machIne
上传时间： 2020-06-10
上传用户：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.
上传时间： 2020-06-10
上传用户：shancjb
随着计算机技术、通信技术的飞速发展和3C(计算机、通信、消费电子)的融合，嵌入式系统已经渗透到各个领域。在32位嵌入式微处理器市场上，基于ARM(Advanced RISC machIne)内核的微处理器在市场上处于绝对的领导地位，因此追踪ARM技术的发展趋势显得尤为重要。在嵌入式操作系统的选择上，Linux一直因其内核精简、代码开放、易于移植等特点受到广大嵌入式系统工程师的青睐。另外，嵌入式系统一旦具备网络接入功能，其信息处理能力更加强大，因此有必要为嵌入式系统构建Web服务器。 本文主要目的是研究基于ARM的嵌入式Linux开发平台构建，并在此基础上进行网络应用程序的开发。 文章深入剖析了ARM9的体系结构，介绍了基于ARM9的S3C2410开发板的特性及资源；阐述了嵌入式操作系统的相关知识及嵌入式Linux移植的基本方法；搭建了移植所需要的开发环境，主要包括在宿主机Linux操作系统下编译arm-linux交叉编译工具等；然后详细阐述了嵌入式Linux开发平台的构建过程，包括对BootLoader的分析和移植，Linux2.6内核的结构分析、代码修改以及内核裁减、配置和移植，网卡驱动程序的移植，以及根文件系统的创建。按文中提供的方法和技巧可以很方便的建立一个ARM-Linux开发平台。 文章最后给出了基于所建平台的网络应用，即在上述所建的软硬件平台上创建Web服务器Boa，并基于Boa进行应用开发。最终实现了基于Boa嵌入式Web服务器的服务器端表单处理程序，实现了PC机与目标板的动态网页交互功能，并且，通过PC机IE浏览器可以直接控制目标板上的硬件和可执行程序，以实现对目标板的远程监控功能。
上传时间： 2013-04-24
上传用户：kernaling
·【英文题名】 Search of Double-fed machIne Variable Speed System Based on DSP 【作者中文名】 沈睿; 【导师】 廖冬初; 【学位授予单位】 湖北工业大学; 【学科专业名称】 控制理论与控制工程 【学位年度】 2007 【论文级别】 硕士 【网络出版投稿人】 湖北工业大学 【网络出版投稿时间】 2008-09-27 【关键词】 双馈调速
上传时间： 2013-04-24
上传用户：hanwu
虚拟机自省（Virtual machIne Introspection，VMI）技术充分利用虚拟机管理器的较高权限，可以实现在单独的虚拟机中部署安全工具对目标虚拟机进行监测，为进行各种安全研究工作提供了很好的解决途径，从而随着虚拟化技术的发展成为一种应用趋势。基于为更深入的理解和更好的应用VMI技术提供参考作用的目的，本文对VMI技术进行了分析研究。采用分析总结的方法，提出了VMI的概念，分析其实现原理和实现方式；详细地分析总结了VMI技术在不同领域的研究进展，通过对不同研究成果根据实现方式进行交叉分析比较，得出不同研究成果对应的4种实现方式；分析了VMI技术面临的语义鸿沟问题；最后对VMI技术研究进行总结和展望。
上传时间： 2014-08-21
上传用户：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..
上传时间： 2013-11-21
上传用户：lchjng
