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They were all near tears. What was left for them to do but have a drink They broke open a bottle of the hard stuff and started to talk. Some cried. Some laughed. The feelings poured out, anger became diffused with laughter, and their intense worry dissipated. The revolt started to take on its rightful context almost immediately, one more colorful event in the history of big, crazy battles they d fought in the service of a common cause. There had been fights before. Screaming matches. Threats and anger. Even power plays. But the hard feelings were usually over pretty quickly. This was up there with the worst, way up there; but in context, it was all part of what it took to survive. By the time they d finished their drinks and clapped backs and parted, they were bonded once more. It was time to go back to work and fight the competition. They were one team against the world again.

In a similar way, the profit-maximising decisions of firms generate the supply curves that affect markets. In 10, we explain how that happens, and we also explain how profit-maximising firms actually go about maximising their profits. If you ve ever had some nasty thoughts about capitalism and who hasn t this chapter puts you eyeball-to-eyeball with the enemy.

Across the system, brokers and associates opened their doors and minds to share their own ideas, needs, and experiences In this way, the project became an embodiment of something they all wanted and wanted to see succeed The content ideas came fast and furious They didn t want talking heads and cheap production standards No one would watch a form of downscale community access television If RE/MAX Satellite Network or RSN as it became known was going to be a success, it needed to be a high-level product, with high-level content and great speakers They divided that content into various areas They wanted to teach technology, knowing, even before the Internet, that computers were one day going to be an important part of an agent s success They wanted to train assistants and sec-.

112 MACHINE LEARNING IN IMAGE PROCESSING AdaBoost and support vector machines (SVMs) are, among others, two very popular and conceptually similar machine learning tools for image processing They are both based on nding a set of hyperplanes to separate the sets of positive and negative examples Current image processing culture involving machine learning for real-time performance almost exclusively uses AdaBoost instead of SVMs AdaBoost is easier to program and has proven itself to work well There are very few papers that deal with real-time detection using SVM principles This makes the AdaBoost approach a better choice for real-time applications A number of recent papers, using both AdaBoost and SVMs, con rm the same, and even apply a two-phase process.

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Often this quotient, which is usually called a Bayes factor, is used explicitly to evaluate different graphs G1 w.r.t. a given reference structure G2 . A commonly used reference structure is a graph without any edges. In the following, however, we confine ourselves to finding an expression for P(G, D). Starting from the formula derived above and applying the product rule of probability theory to the density f(G, ), we arrive at P(G, D) =

Most windows are processed in the rst phase by AdaBoost, and in the second phase, an SVM is used on dif cult cases that could not be easily eliminated by AdaBoost This way, the real-time constraint remains intact Le and Satoh [16] maintain that The pure SVM has constant running time of 554 windows per second (WPS) regardless of complexity of the input image, the pure AdaBoost (cascaded with 37 layers 5924 features) has running time of 640, 515 WPS If a pure SVM approach was applied to our test set, it would take 17, 500, 000/554 9 h of pure run time to test the 106 images It would take roughly 2 min to process an image of size 320 240 Thus, Lee and Satoh [16] claim that cascaded AdaBoost is 1000 times faster than SVMs.

Accordingly, for a three-month Treasury bill purchased for $989.20, its annualized total return is 4.43 percent. The second term, 365/90, is the annualization term. We assume 365 days in a year (366 for a leap year), and

The persistence manager is responsible for implementing and executing find methods based on a new query language called EJB Query Language (EJB QL) The structure of the QL statements depends on the find methods coded in the home interface of the entity bean and the relationships of fields between beans coded in the deployment descriptor The EJB QL defines query methods (find and select methods) for entity beans with container-managed persistence EJB QL defines query methods so that they are portable across containers and persistence managers EJB QL is a declarative, SQL-like language intended to be compiled to the target language of the persistent data store used by a persistence manager EJB QL is based on a subset of SQL92 and is enhanced by path expressions that allow navigation over the relationships defined for entity beans and dependent object classes.