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In this chapter, we ll rst tell you a little about COM add-ins Then, we ll show you how to create your own add-in that will enable you to copy XML data from one form and paste it into another Once you have a good understanding of the basics, we ll show you how to combine a COM addin with an ctiveX control to create your own Custom Task Pane that will show you the XML structure and data in the currently opened InfoPath form Finally, we ll show you how to create managed add-ins with Visual Studio 2005 Tools for the 2007 Microsoft Of ce System (VSTO 2005 SE). Painting DataMatrix In VS .NET Using Barcode maker for ASP .IDTExtensibility2 Interface In order for a component to be considered a COM add-in, it must implement the IDTExtensibility2 interface (see the MSDN reference listed in he Appendix) Through this interface, the add-in is alerted of various events that occur in the application, such as when the application is started, when it is shut down, when the add-in is loaded, and so on Table 201 lists the methods of the IDTExtensibility2 interface It is through the OnConnection method of the IDTExtensibility2 interface that the COM add-in obtains access to the application object of the hosting application The application object is one of those shared.Related: C# QR Code Generator , Java QR Code Generator , QR Code Generator Java Image

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Is the excel format 2007+? You are going to need to look into automation by using Excel interop:

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speci ed by the VC-dimension [152, 732] Cohn and Tesauro [152] show that for experiments conducted, the generalization error decreases exponentially with the number of examples, rather than the 1/PT result of the VC bound Experimental results by Lange and M nner [502] show that more training examples do not necessarily improve a generalization In their paper, Lange and M nner introduce the notion of a critical a training set size Through experimentation they found that examples beyond this critical size do not improve generalization, illustrating that excess patterns have no real gain This critical training set size is problem dependent While enough information is crucial to e ective learning, too large training set sizes may be of disadvantage to generalization performance and training time [503, 948] Redundant training examples may be from uninteresting parts of input space, and do not serve to re ne learned weights it only introduces unnecessary computations, thus increasing training time Furthermore, redundant examples might not be equally distributed, thereby biasing the learner The ideal, then, is to implement structures to make optimal use of available training data That is, to select only informative examples for training, or to present examples in a way to maximize the decrease in training and generalization error To this extent, active learning algorithms have been developed Cohn et al [151] de ne active learning (also referred to in the literature as example selection, sequential learning, query-based learning) as any form of learning in which the learning algorithm has some ontrol over what part of the input space it receives information from An active learning strategy allows the learner to dynamically select training examples, during training, from a candidate training set as received from the teacher (supervisor) The learner capitalizes on current attained knowledge to select examples from the candidate training set that are most likely to solve the problem, or that will lead to a maximum decrease in error Rather than passively accepting training examples from the teacher, the network is allowed to use its current knowledge about the problem to have some deterministic control over which training examples to accept, and to guide the search for informative patterns By adding this functionality to a NN, the network changes from a passive learner to an active learner Figure 77 illustrates the di erence between active learning and passive learning With careful dynamic selection of training examples, shorter training times and better generalization may be obtained Provided that the added complexity of the example selection method does not exceed the reduction in training computations (due to a reduction in the number of training patterns), training time will be reduced [399, 822, 948] Generalization can potentially be improved, provided that selected examples contain enough information to learn the task Cohn [153] and Cohn et al [151] show through average case analysis that the expected generalization performance of active learning is signi cantly better than passive learning Seung et al [777], Sung and Niyogi [822] and Zhang [948] report similar improvements Results presented by Seung et al indicate that generalization error decreases more rapidly for active learning than for passive learning [777] Two main approaches to active learning can be identi ed, ie incremental learning.

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Convert Excel file to PDF in C# and VB.NET. GemBox.Spreadsheet enables you to easily convert an Excel file to different file formats in C# and VB.NET. For example, you can convert Excel file to a different spreadsheet format (XLSX, XLS, ODS, CSV, or HTML) or to PDF, XPS, and image formats.

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