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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) =.

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EDUCATION PhD, 1992, in American History Research interests: Transdisciplinary approaches to health care issues of affluent women in twentieth-century America: 1970 1990 MA, 1987, Women s Studies, University of Wisconsin, Madison, WI Thesis: Oral histories of millionaire widows in Philadelphia, PA, Miami, FL, and Phoenix, AZ

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Q567731 - ASP.NET PDF viewer control | DevExpress Support Center
Feb 3, 2014 ยท Technology: .NET, Platform: ASP.NET MVC, Type: Question, Subject: ASP.NET PDF viewer control.

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

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Before you get going on the coding, you need to bring in two other frameworks: OpenGL.framework and GLUT.framework (the latter is there just to provide access to the teapot drawing functions). To add these frameworks, right-click on the project name in the Groups and Files list and choose Add Existing Frameworks. Select OpenGL.framework and GLUT.framework (use the Command key to select both at once), then choose the Add button. Now the workspace shows the added frameworks. You need to start by opening up TumblingTeapot.h and adding the variables you will need (see the bold text in Listing 5 2).

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

There are a number of good sources for reading about the latest research results in machine learning Relevant journals include Machine Learning, Neural Computation, Neural Networks, Journal of the American Statistical Association, and the IEEE Transactions on Pattern Analysis and Machine Intelligence There are also numerous annual conferences that cover different aspects of machine learning, including the International Conference on Machine Learning, Neural Information Processing Systems, Conference on Computational Learning Theory, International Conference on Genetic Algorithms, International Conference on Knowledge Discovery and Data Mining, European Conference on Machine Learning, and others

Listing 5 2. TumblingTeapot.h #import <ScreenSaver/ScreenSaver.h> @interface TumblingTeapotView : ScreenSaverView { NSOpenGLView *glView; GLfloat rotation; int displayList; } - (void)startUpOpenGL; @end

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Display pdf in a div after getting it from sql | The ASP.NET Forums
Hi all I have this very simple little page where I get a pdf file from a SQL Server database and display that file on the page. Here is the code.

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