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If you have programmed in a computer language other than Perl, try reading this book with the help of the online documentation and tutorials. Because this book focuses on a subset of Perl that is most useful for text mining, there are commands and functions that you might want to use but are not discussed here. If you already program in Perl, then peruse the listings in chapters 2 and 3 to see if there is anything that is new to you. These two chapters contain the core Perl knowledge needed for the rest of the book, and once this is learned, the other chapters are understandable. After chapters 2 and 3, each chapter focuses on a topic of text mining. All the later chapters make use of these two chapters, so read or peruse these first. Although each of the later chapters has its own topic, these are the following interconnections. First, chapter 7 relies on chapters 4 and 5. Second, chapter 8 uses the idea of PCA introduced in chapter 7. Third, there are many examples of later chapters referring to the computer programs or output of earlier chapters, but these are listed by section to make them easy to check. The Perl programs in this book are divided into code samples andprogrums. The former are often intermediate results or short pieces of code that are useful later. The latter are typically longer and perform a useful task. These are also boxed instead of ruled. The results of Perl programs are generally called outputs. These are also used for R programs since they are interactive. Finally, I enjoy analyzing text and believe that programming in Perl is a great way to do it. My hope is that this book helps share my enjoyment to both students and researchers.

(4 kO in parallel with 12 kO)

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Setting a transparent color sounds like a great idea, but in reality it does not work as well with photos as it does with clip art. For one thing, it replaces all instances of that color, not just in the background. So, for example, if you have a picture of a man with a white shirt on a white background and choose to make white the transparent color (because you want to drop out the background), the man s shirt becomes transparent, too. Another reason it doesn t work that well on photos is that what looks like one color in a photo is not usually just one color. Think of a blue sky, for example. It probably consists of at least two dozen different shades of blue. If you try to make one of those shades of blue transparent using PowerPoint s transparency tool, you ll probably just end up with splotches of transparent areas. So what s the solution In previous versions of PowerPoint, there wasn t much you could do. You had to use alpha channels in a third-party image-editing program to create true transparency and save the image as TIF or PNG. (JPEG format does not support alpha channels.) In PowerPoint 2010, however, there is a Remove Background command that can do the trick in many cases. To remove the background, select the picture and then choose Picture Tools Format Remove Background. The Background Removal tab becomes available, and the areas of the image that PowerPoint plans to remove appear with a purple wash over them (shown in Figure 13-18).

You learn about creating masks from selections in 9. This preview feature is great. It allows you to create the selection you need without lots of trial and error. It also lets you see how one setting can be used to tweak the modi cations of other settings. The biggest drawback I see is that I end up playing with this tool more than I need to because it s so much fun to compare and contrast different edge re nement scenarios.

of the simulated CTMC is in some (given) subset 3 from 1. We start simulating at t, = 0 until some value t,; we only start collecting samples after the initial transient period of length ti is over. 1. What is a useful estimator for the measure of interest 2. Outline a simulation CTMCs. program for CTMC simulation, thereby using properties of

is called the Schur complement of M with respect to C. This is a general matrix result; it is an immediate extension to Hermitian matrices of a similar factorization result that was described in a footnote in Sec. 1.4 for symmetric matrices. The validity of (3.11) can be verified by expanding the right-hand side. The factorization (3.11) for A is valid as long 4 as the matrix C is invertible, which means that we cannot apply the result directly to the center matrix

Cluster means : [ , I 1 [,21 1 0.5 0.5 2 5.5 5.5 Clustering vector: [ 1 3 1 1 1 1 2 2 2 2 Within cluster sum of squares by cluster: [I1 2 2

FIGURE 18.41 Holding down Alt (Option on the Mac) while using the Exposure slider (see this slider in Figure 18.38) brings up a graphic display of which pixels are being clipped to pure white (right). In this case, the brightest clouds are clipping.

FIGURE 11.10 The Save As dialog box offers options for saving your document in a target Version Cue space.

15 10 20 35 30 25 Average Channel SNR y (dB)

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