slt 中文意思是什麼

Chapter 2 has systematically discussed machine learning problem, which is the basic of svm, with statistical learning theory or slt. secondly, chapter 3 has educed the optimal hyperplane from pattern recognition

第二章探討了支持向量機理論基礎? ?學習問題，尤其是對vapnik等人的統計學習理論（ slt ）結合學習問題作了系統的闡述。

According to the different sample set, we have been on discussion, using lagrangian multiplier technique or lmt in the optimal theory, slt and function analysis ， then we get the decision function and svm with the corresponding different sample set. thirdly, for improving generalization ability, application ability and recognition speed of svm, we have used fuzzy set theory ( fst ) and rough set theory to study svm deep, and integrated them into svm, constructed fssvm ( fuzzy set svm ) and svm based on rough set theory, and extended performances of svm in the chapter 4, 5

再次，為了進一步提高支持向量機的通用性以及推廣能力、應用能力、識別速度等性能，在第四、第五兩章運用模糊集理論（ fst ）和粗糙集理論（ rst ）對支持向量機進行研究，採用優勢互補原則，先是把模糊集與支持向量機有機結合，構造出基於模糊集的支持向量機（ fssvm ） ，然後把粗糙集理論與支持向量機相互結合，進而把rst與fssvm相互結合，構造出基於rst的支持向量機。

Basing on the statistical inaming t ' heory ( slt ), the thesis discusses the svm problems in linearly separable case, lineariy non - separable case and non - linear separable case, and induces a convex quadratic programming ( qp ) problem with an equation constrain and non - equation constrains. then one program on solving the op problem is proposed

概述了統計學習理論的主要內容，推導了支持向量機方法在文本線性可分、線性不可分和非線性可分情況下實現分類的數學公式，將學習問題轉化為一個在等式約束和不等式約束下的凸二次優化問題，總結了求解的過程。

Being different from traditional neural network or nn, nn is based on traditional statistics, which provides conclusion only for the situation where sample size is tending to infinity, while svm is based on statistical learning theory or slt, which is a small - sample statistics and concerns mainly the statistic principles when sample are limited, especially the properties of learning procedure

支持向量機（ svm ）是九十年代中期發展起來的新的機器學習技術，與傳統的神經網路（ nn ）技術不同， svm是以統計學習理論（ slt ）為基礎， nn是以傳統統計學理論為基礎。傳統統計學的前提條件是要有足夠多的樣本，而統計學習理論是著重研究小樣本條件下的統計規律和學習方法的，它為機器學習問題建立了一個很好的理論框架。

The dissertation mainly aims at applying several active machine learning strategies to intrusion detection and systematically studies signal analysis techniques of intrusion detection based on statistical learning theory ( slt ), symbol inductive learning theory and genetic learning method. meanwhile, performance comparison and evaluation among intrusion detection techniques based on different machine learning strategies are presented according to computational learning theory and statistical hypothesis test methodology. intrusion detection is regarded as a pattern recognition problem in term of statistical learning theory ; i

本文的主要工作是將目前幾種有生命力的機器學習策略應用於入侵檢測技術中，論文從入侵檢測的不同視角出發，系統深入地研究了統計學習理論、基於符號的歸納學習理論和遺傳學習方法在入侵檢測信號分析中的應用技術，並在可能近似正確（ pac ）學習框架下，利用計算學習理論和統計假設檢驗方法對基於不同機器學習策略的入侵檢測方法進行了性能比較和評估。

With the development of communication, information and electronic technology and computer network, intelligent transport system ( its ) is paid more and more emphasis, it contains many parts, such as vehicle type recognition and license plate recognition. in this paper, we introduce svm to the field of its, the main work is described as follows : ( 1 ) we summarize the latest research achievements and development of its, present the conceptions of slt and the principles of svm ; ( 2 ) taking the traffic sign as examples and adopting hough transform in the stage of feature extraction, we introduce svm to the problem of shape recognition and compare the experimental results with traditional learning methods. ( 3 ) then we use svm to settle the vehicle type recognition problem, where we utilize the wavelet analysis and mathematical morphology method to extract the figure feature

本文將支持向量機引入智能交通系統領域，主要進行的工作如下： （ 1 ）整理總結了國內外學術界關于統計學習理論方面的研究成果，介紹統計學習理論的基本概念和支持向量機的基本原理； （ 2 ）在形狀識別問題中以交通標志圖像作為實驗對象，利用hough變換進行特徵提取，在識別階段利用支持向量機方法進行分類，並與神經網路等傳統學習方法對比； （ 3 ）將支持向量機應用於車型識別問題中，針對收費站採集的汽車圖像，首先採用小波分析和數學形態學的方法提取其外形特徵，在識別階段利用支持向量機方法進行分類，並與其他傳統學習方法進行了對比； （ 4 ）將支持向量機應用於車牌識別問題中，車牌識別包括車牌定位、車牌字元分割以及字元識別三個步驟，先採用數學形態學方法對車牌區域進行定位，然後採用top - hat變換等方法分割車牌字元，在識別階段採用支持向量機演算法進行字元識別，取得了較為滿意的結果。

In this essay, some important concepts about slt are firstly introduced i. e. srm and vc dimension to illustrate that svm holds excellent capability of generalization. in addition, the concept and process of answering of svm is also introduced to illustrate that svm is a sort of convex optimization issue whose answer has characteristics of global optimum

本文首先引入了統計學習理論的一些關鍵概念:結構最小化原則和vc維等，以說明支持向量機具有良好的推廣性能，接著介紹了支持向量機的概念和求解過程，從中可看出支持向量機是一種凸優化問題，其解具有全局最優的特點。

Shiga - like toxin, slt

志賀樣毒素

Browse the first chapters of this book if you want to go deeper into the foundations of slt

如果你想要更深入的了解統計學習的基本原理，請瀏覽本書的前幾章節。

Support vector machine ( svm ) is used as the implementation basis, which is a tool of statistical learning theory ( slt )

在探索手寫字元識別的方法上採用了統計學習理論，利用支撐向量機svm作為基本的識別工具。

Slt is a machine learning theory based on samples, which was started by v. vapnik in the 1970s and matured to form a complete theoretical architecture in the middle of 1990s

V . vapnik等人從六、七十年代開始致力於此方面的研究，到九十年的中期，其理論的不斷發展和成熟，已基本形成一套比較完整的理論體系。

( 4 ) support vector machine ( svm ) is a novel powerful learning machine, which can solve small - sample learning problem better. the basic ideas of statistical learning theory ( slt ) and svm are introduced, and the characteristics of svm are illuminated

本文參考前人的工作，對統計學習理論和支持向量機的相關知識進行了介紹，分析了svm模型的特點，並對選用不同的模型和參數對支持向量機模型的影響進行了探討。

Statistical learning theory ( slt ) is based on the structural risk minimization ( srm ) principle, and it is a new set of theory system, which specially aims at machine learning issues under the circumstances of small - sample. based on this slt, supporting vector machine ( svm ) method has been developed as a new machine learning algorithm and also practical applications of slt

統計學習理論建立在結構風險最小化原則基礎上，它是專門針對少樣本情況下機器學習問題而建立的一套新的理論體系，支持向量機就是在統計學習理論這一基礎上發展起來的一種新的機器學習演算法，它是統計學習理論的具體應用。

First, a research is done in this paper about error correction methods of network analyzer, including the analysis of the network analyzer principle, the establishment of error model and the acquirement of standard calibration method and nonstandard calibration method. based on 12 - error module, the solt ( short + open + load + thru ) method which is standard calibration one and the slt ( short + line + thru ) method which is nonstandard calibration one are obtained

本文在分析s參數的測量誤差的基礎上，研究了網路分析儀誤差校正方法，包括分析網路分析儀的結構、工作原理和測量方法，用已有的網路分析儀測量s參數時的誤差理論建立誤差模型，構造其相應的標準件校正方法和非標準件校正方法。

Summarize the theories, research methods and developing history of economic early warning. discuss the warning nature of usual early warning system including classical and new warning system and establish the frame pattern of warning system. analyze the basic theory and character of statistic learning theory ( slt ) and svm

對經濟預警的理論、研究方法和發展歷史進行了回顧和綜述；詳細討論了傳統預警系統的預警本質，包括經典預警理論和新預警理論及其預警系統，建立了預警系統框架範式；深入分析了統計學習理論和支持向量機的基本理論和特點。

Then, 8 - error module is established, and the osl ( open + short + load ) method and the slt method which are standard calibration ones and the tls ( thru + line + short ) method which is nonstandard calibration one are obtained on the basis of the error module. good measurement results can be acquired by using nonstandard calibration technology that is proved by experiment. secondly, oddity point maybe appear in the measurement results after network analyzer is corrected by nonstandard calibration technology

用十二項誤差模型和八項誤差模型的非標準校正方法對測量系統進行誤差校正後，測量結果一般都會出現奇異點問題，且奇異點一般出現在傳輸線傳輸參數x相位的過0點，這可能與這些方法都使用了未知參數的傳輸線有關。

In this thesis, several issues concerning the machine learning and the classification of high dimensional multispectral data with limited training samples are addressed, which are based on statistic learning theory ( slt ), support vector machine ( svm ) and artificial neural networks ( ann ). the mai n work and results are outlined as follows : 1. the characteristics of high dimensional multispectral data are studied, and the difficulties that deteriorate the performance of the traditional pattern classification algorithms are carefully analyzed

以統計學習理論（ statisticlearningtheory ? slt ） 、支持向量機（ supportvectormachine ? svm ）和人工神經網路（ artificialneuralnetworks ? ann ）為基礎，本文開展了以下幾個方面的研究工作：深入分析了高維多光譜數據的特點和傳統模式分類方法在高維多光譜數據分類中面臨的困難。