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[Other resource] Herbrich-Learning-Kernel-Classifiers-Theory-and-Al

Description: Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples.
Platform: | Size: 2537081 | Author: google2000 | Hits:

[Delphi VCL] FastReport.v4.7.49

Description:

Current version
---------------
+ added confirmation reading for TfrxMailExport
+ added TimeOut field to TfrxMailExport form
- fixed bug whih PDF export in Delphi4 and CBuilder4
- fixed bug with some codepage which use two bytes for special symbols (Japanese ans Chinese codepages)
- fixed bug when engine delete first space from text in split Memo
+ added ability to use keeping(KeepTogether/KeepChild/KeepHeader) in multi-column report
- fixed bug in multi-column page when band overlap stretched PageHeader
- fixed bug with using ReprintOnNewPage
+ added ability to split big bands(biggest than page height) by default

version 4.7
---------------
+ CodeGear RAD Studio (Delphi/C++Builder) 2009 support
+ [enterprise] enchanced error description in logs
+ added properties TfrxHTMLExport.HTMLDocumentBegin: TStrings,
TfrxHTMLExport.HTMLDocumentBody: TStrings, TfrxHTMLExport.HTMLDocumentEnd: TStrings
+ improved RTF export (with line spacing, vertical gap etc)
+ added support of Enhanced Metafile (EMF) images in Rich Text (RTF), Open Office (ODS), Excel (XLS) exports
+ added OnAfterScriptCompile event
+ added onLoadRecentFile Event
+ added C++ Builder demos
+ added hot-key Ctrl + mouseWheel - Change scale in designer
+ added TfrxMemoView.AnsiText property
- fixed bug in RTF export with EMF pictures in OpenOffice Writer
- fixed some multi-thread isuues in engine, PDF, ODF exports
- [enterprise] fixed integrated template of report navigator
- [enterprise] fixed bug with export in Internet Explorer browser
- fixed bug with font size of dot-matix reports in Excel and XML exports
- fixed bug in e-mail export with many addresses
- fixed bug in XLS export (with fast export unchecked and image object is null)
- [enterprise] fixed bug in TfrxReportServer.OnGetVariables event
- fixed bug in Calcl function
- fixed memory leak in Cross editor
- fixed progress bar and find dialog bug in DualView
- fixed bug in PostNET and ean13 barcodes
- fixed bug with TruncOutboundText in Dot Matrix report
- fixed bugs with break points in syntaxis memo
- improved BeforeConnect event in ADO
- fixed bug in inhehited report with internal dataset
- fixed bug in TfrxPanelControl with background color(Delphi 2005 and above)

经查证后，这个是最新的 FastReport.v4.7.49
 


Platform: | Size: 2986536 | Author: gzhubin | Hits:

[ADO-ODBC] 一个增强的数据库类CDataSet

Description: 一个增强的数据库类CDataSet 因为MFC完全支持数据库应用程序的开发，所以大多数数据库应用都使用CDatabase和CRecordset类，并且类向导（Class Wizard）提供了快速简易的方式来使用这两个类。有一点不足的就是当应用程序涉及到多表数据库时，类向导将产生大量的关于记录集的源码文件使得工程（project）给人的感觉很混乱。 本文介绍如何使用一个模板记录集类来降低类向导所产生的记录集文件的数量，同时增强记录积类(CRecordset)的功能。这个模板记录集类叫做：CDataSet。它的主要目的是降低代码量，为数据对象数组提供一个接口。-an enhanced database category CDataSet because MFC fully support database application development, the majority of database applications used CDatabase and CRecordset class and category Wizard (Class Wizard) provides a fast and easy way to use these two categories. There is one point when the shortage of applications related to multi-table database, the wizard class will have a lot of records set on the source document makes the project (project) gives the impression that very confused. This paper describes how to use a template Records Set to reduce class wizard from the record set in the volume of documentation, while enhancing the plot record category (CRecordset) function. The template called Set Records : CDataSet. Its main purpose is to reduce the amount of code and data objects to p
Platform: | Size: 33792 | Author: 杨葶 | Hits:

[Other] Herbrich-Learning-Kernel-Classifiers-Theory-and-Al

Description: Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples.
Platform: | Size: 2536448 | Author: | Hits:

[VC/MFC] prtools_ac

Description: 一个关于模式识别的代码 不错 对初学者很值得推荐 -On Pattern Recognition code good for beginners is very worthy of recommendation
Platform: | Size: 696320 | Author: lilong | Hits:

[Algorithm] Matlabcodes-RobustPCA

Description: Matlab codes for Robust PCA multivariate control chart-Robust PCA multivariate control chart mainly consists two steps: Step1 Calculates the robust mean and the robust covariance of original dataset using the minimum covariance determinant (MCD). In MCD technique, finding a subset containing half of the data such that its covariance matrix has the lowest determinant, then using this subset to calculate the robust mean and the robust covariance matrix (Hubert, Rousseeuw, & Branden, 2005) Step2 Standardize data using robust mean and robust standard deviation from Step1. Apply PCA analysis, calculate the principalcomponent score matrix Y=ZA, where Z is the robust standardized data matrix, and A is p*p matrix of eigenvectors (also called principalcomponents)
Platform: | Size: 1024 | Author: Jianxin Zhang | Hits:

[WEB Code] PHP_DataSet

Description: 用PHP模拟实现类似asp.net的DataSet的功能。-Asp.net using PHP simulation of the DataSet to achieve a similar function.
Platform: | Size: 5120 | Author: wubiyou | Hits:

[Disk Tools] matlab-r2012b-license-key123

Description: lear all A=wk1read( cigarette.wk1 ,1,0) W1=wk1read( Spat-Sym-US.wk1 ) Dataset downloaded from www.wiley.co.uk/baltagi/ Source: Baltagi and Levin (1992) and Baltagi, Griffin and Xiong (2000). Description: Panel Data, 46 U.S. States over the period 1963-1992. Spatial weights matrix constructed by Elhorst written by: J.Paul Elhorst 9/2004 University of Groningen Department of Economics 9700AV Groningen the Netherlands j.p.elhorst@eco.rug.nl -lear all A=wk1read( cigarette.wk1 ,1,0) W1=wk1read( Spat-Sym-US.wk1 ) Dataset downloaded from www.wiley.co.uk/baltagi/ Source: Baltagi and Levin (1992) and Baltagi, Griffin and Xiong (2000). Description: Panel Data, 46 U.S. States over the period 1963-1992. Spatial weights matrix constructed by Elhorst written by: J.Paul Elhorst 9/2004 University of Groningen Department of Economics 9700AV Groningen the Netherlands j.p.elhorst@eco.rug.nl
Platform: | Size: 13312 | Author: xym234 | Hits:

[AI-NN-PR] machine-learning_PCA

Description: 环境为winpython 32bit 2.7.5.3 p = PCA() print u"均值化后的数据集为：",p.dataset( H:\\PCA_test.txt ) print u"协方差矩阵为：",p.COV() print u"特征向量为：",p.eig_vector()[1] tt = p.pc(dim=1) print "tt:",tt print u"新的维度数据集",tt[1]- """ Principal components analysis,PCA,COV,eig,eig vector Parameters¶ : path：数据集的存放路径 dim : 数据降维后的维度数 Attribute: means_data : 原数据- 原数据均值化 m : 数据集的行数 n ：数据集的列数 cov_matrix : 协方差矩阵 eig_vectors : 协方差矩阵的特征向量 eig_value : 协方差矩阵求得的特征值 cum_P : 排序后的特征值, 累积百分比计算 Method: PCA.dataset():数据集导入, return：means_data,array(m,n) PCA.cov_matrix:协方差矩阵计算, retrn:cov_matrix,array(n,n) PCA.eig_vector:特征值和特征向量计算, return:(eig_value, eig_vectors),(array(1xn),array(n,n)) PCA.pc:降维后的数据集计算, return:data_rescaled,array(m,dim), defaut:dim=2 """
Platform: | Size: 2048 | Author: hhkk | Hits:

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