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(biāo)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(biāo)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(biāo)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(biāo)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(biāo)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(jī)技術(shù)、通信技術(shù)的飛速發(fā)展和3C(計算機(jī)、通信、消費(fèi)電子)的融合，嵌入式系統(tǒng)已經(jīng)滲透到各個領(lǐng)域。在32位嵌入式微處理器市場上，基于ARM(Advanced RISC Machine)內(nèi)核的微處理器在市場上處于絕對的領(lǐng)導(dǎo)地位，因此追蹤ARM技術(shù)的發(fā)展趨勢顯得尤為重要。在嵌入式操作系統(tǒng)的選擇上，Linux一直因其內(nèi)核精簡、代碼開放、易于移植等特點(diǎn)受到廣大嵌入式系統(tǒng)工程師的青睞。另外，嵌入式系統(tǒng)一旦具備網(wǎng)絡(luò)接入功能，其信息處理能力更加強(qiáng)大，因此有必要為嵌入式系統(tǒng)構(gòu)建Web服務(wù)器。 本文主要目的是研究基于ARM的嵌入式Linux開發(fā)平臺構(gòu)建，并在此基礎(chǔ)上進(jìn)行網(wǎng)絡(luò)應(yīng)用程序的開發(fā)。 文章深入剖析了ARM9的體系結(jié)構(gòu)，介紹了基于ARM9的S3C2410開發(fā)板的特性及資源；闡述了嵌入式操作系統(tǒng)的相關(guān)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(fā)環(huán)境，主要包括在宿主機(jī)Linux操作系統(tǒng)下編譯arm-linux交叉編譯工具等；然后詳細(xì)闡述了嵌入式Linux開發(fā)平臺的構(gòu)建過程，包括對BootLoader的分析和移植，Linux2.6內(nèi)核的結(jié)構(gòu)分析、代碼修改以及內(nèi)核裁減、配置和移植，網(wǎng)卡驅(qū)動程序的移植，以及根文件系統(tǒng)的創(chuàng)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(fā)平臺。 文章最后給出了基于所建平臺的網(wǎng)絡(luò)應(yīng)用，即在上述所建的軟硬件平臺上創(chuàng)建Web服務(wù)器Boa，并基于Boa進(jìn)行應(yīng)用開發(fā)。最終實(shí)現(xiàn)了基于Boa嵌入式Web服務(wù)器的服務(wù)器端表單處理程序，實(shí)現(xiàn)了PC機(jī)與目標(biāo)板的動態(tài)網(wǎng)頁交互功能，并且，通過PC機(jī)IE瀏覽器可以直接控制目標(biāo)板上的硬件和可執(zhí)行程序，以實(shí)現(xiàn)對目標(biāo)板的遠(yuǎn)程監(jiān)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(dǎo)師】 廖冬初; 【學(xué)位授予單位】 湖北工業(yè)大學(xué); 【學(xué)科專業(yè)名稱】 控制理論與控制工程 【學(xué)位年度】 2007 【論文級別】 碩士 【網(wǎng)絡(luò)出版投稿人】 湖北工業(yè)大學(xué) 【網(wǎng)絡(luò)出版投稿時間】 2008-09-27 【關(guān)鍵詞】 雙饋調(diào)速
標(biāo)簽： DSP 雙饋 電機(jī)調(diào)速系統(tǒng)
上傳時間： 2013-04-24
上傳用戶：hanwu
虛擬機(jī)自省（Virtual Machine Introspection，VMI）技術(shù)充分利用虛擬機(jī)管理器的較高權(quán)限，可以實(shí)現(xiàn)在單獨(dú)的虛擬機(jī)中部署安全工具對目標(biāo)虛擬機(jī)進(jìn)行監(jiān)測，為進(jìn)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(shù)的發(fā)展成為一種應(yīng)用趨勢。基于為更深入的理解和更好的應(yīng)用VMI技術(shù)提供參考作用的目的，本文對VMI技術(shù)進(jìn)行了分析研究。采用分析總結(jié)的方法，提出了VMI的概念，分析其實(shí)現(xiàn)原理和實(shí)現(xiàn)方式；詳細(xì)地分析總結(jié)了VMI技術(shù)在不同領(lǐng)域的研究進(jìn)展，通過對不同研究成果根據(jù)實(shí)現(xiàn)方式進(jìn)行交叉分析比較，得出不同研究成果對應(yīng)的4種實(shí)現(xiàn)方式；分析了VMI技術(shù)面臨的語義鴻溝問題；最后對VMI技術(shù)研究進(jìn)行總結(jié)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..
標(biāo)簽： 1099 LTC 8位 AD轉(zhuǎn)換
上傳時間： 2013-11-21
上傳用戶：lchjng
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