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To summarize, the naive Bayes classification VNB is the classification that maximizes the probability of observing the words that were actually found in the document, subject to the usual naive Bayes independence assumption The independence assumption P(al, alll lvj) = P(ai lvj) states in this setting that the word probabilities for one text position are independent of the words that occur in other positions, given the document classification vj Note this assumption is clearly incorrect For example, the probability of observing the word "learning" in some position may be greater if the preceding word is "machine" Despite the obvious inaccuracy of this independence assumption, we have little choice but to make it-without it, the number of probability terms that must be computed is prohibitive Fortunately, in practice the naive Bayes learner performs remarkably well in many text classification problems despite the incorrectness of this independence assumption Dorningos and Pazzani (1996) provide an interesting analysis of this fortunate phenomenon To calculate VNB using the above expression, we require estimates for the probability terms P(vj) and P(ai = wklvj) (here we introduce w to indicate the kth k word in the English vocabulary) The first of these can easily be estimated based on the fraction of each class in the training data (P(1ike) = 3 and P(dis1ike) = 7 in the current example) As usual, estimating the class conditional probabilities (eg, P(al = "our"ldis1ike)) is more problematic because we must estimate one such probability term for each combination of text position, English word, and target value Unfortunately, there are approximately 50,000 distinct words in the English vocabulary, 2 possible target values, and 111 text positions in the current example, so we must estimate 2 111 -50,000 = 10 million such terms from the training data Fortunately, we can make an additional reasonable assumption that reduces the number of probabilities that must be estimated In particular, we shall assume the probability of encountering a specific word w (eg, "chocolate") is k independent of the specific word position being considered (eg, a23 versus agg) More formally, this amounts to assuming that the attributes are independent and identically distributed, given the target classification; that is, P(ai = wk)vj) =. code 128 algorithm c# GenCode128 - A Code128 Barcode Generator - CodeProject
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This isn't a direct answer BUT I would strongly recommend to use a well-tested library for generating barcodes ... getting barcodes right isn't ... pressed extremely efficiently Large groups of blocks that move together, such as large objects or the entire picture panning sideways, are also compressed efficiently MPEG uses three kinds of picture storage methods Intra pictures are like JPEG pictures, in which the entire picture is compressed and stored with DCT quantization This creates a reference, or information, frame from which successive pictures are built These I frames also allow random access into a stream of video and in practice occur about twice a second Predicted pictures, or P frames, contain motion vectors describing the difference from the closest previous I frame or P frame If the block has changed slightly in intensity or color (remember, frames are separated into three channels and compressed separately), then the difference (error) is also encoded If something entirely new appears that does not match any previous blocks, such as a person walking into the scene, then a new block is stored in the same way as in an I frame If the entire scene changes, as in a cut, the encoding system is usually smart enough to make a new I frame The third storage method is a bidirectional picture, or B frame The system looks both forward and backward to match blocks In this way, if something new appears in a B frame, it can be matched to a block in the next I frame or P frame Thus P and B frames are much smaller than I frames Experience has shown that two B frames between each I or P frame work well A typical second of MPEG video at 30 frames per second looks like I B B P B B P B B P B B P B B I B B P B B P B B P B B P B B (Figure 34) Obviously, B frames are much more complex to create than P frames, requiring time-consuming searches in both the previous and subsequent I or P frame For this reason, some real-time or low-cost MPEG encoders only create I and P frames Likewise, I frames are easier to create than P frames, which require searches in the subsequent I or P frame Therefore, the simplest. c# code 128 source Code 128 Barcode Generator for Microsoft Visual C# .NET
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GenCode128 - A Code128 Barcode Generator ... It supports major 1D and 2D barcodes including Code 128 and QR Code. Supported .... NET code in VB or C# . Machine learning addresses the question of how to build computer programs that improve their performance at some task through experience Major points of this chapter include: Machine learning algorithms have proven to be of great practical value in a variety of application domains They are especially useful in (a) data mining problems where large databases may contain valuable implicit regularities that can be discovered automatically (eg, to analyze outcomes of medical treatments from patient databases or to learn general rules for credit worthiness from financial databases); (b) poorly understood domains where humans might not have the knowledge needed to develop effective algorithms (eg, human face recognition from images); and (c) domains where the program must dynamically adapt to changing conditions (eg, controlling manufacturing processes under changing supply stocks or adapting to the changing reading interests of individuals) Machine learning draws on ideas from a diverse set of disciplines, including artificial intelligence, probability and statistics, computational complexity, information theory, psychology and neurobiology, control theory, and philosophy A well-defined learning problem requires a well-specified task, performance metric, and source of training experience Designing a machine learning approach involves a number of design choices, including choosing the type of training experience, the target function to be learned, a representation for this target function, and an algorithm for learning the target function from training examples [selfview setAlpha:07f]; . Learning involves search: searching through a space of possible hypotheses to find the hypothesis that best fits the available training examples and other prior constraints or knowledge Much of this book is organized around different learning methods that search different hypothesis spaces (eg, spaces containing numerical functions, neural networks, decision trees, symbolic rules) and around theoretical results that characterize conditions under which these search methods converge toward an optimal hypothesis The doIt method also then gets a reference to the application s window and adds myActivity s view above the ActivityAndProgressViewController s view code 128 generator c# GenCode128 - A Code128 Barcode Generator - CodeProject
10 Jun 2006 ... Create Code128 barcodes for WinForms or ASP.NET. ... If TDD in C# has developed a good answer to that, I haven't yet stumbled upon it. code 128 c# font Code 128 C# Barcode Generator Library ... - BarcodeLib.com
Developer guide for generating Code 128 barcode images in .NET applications using Visual C# . Code 128 C# barcoding examples for ASP.NET website ...
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