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Of particular interest are cases where the training set is not easily available, and most of it needs to be manually created As a particular case study, we will see the application of machine learning to the detection of rears of cars in images [24,25] Speci cally, the system is able to recognize cars of a certain type such as a Honda Accord 2004 While Hondas have been used as an instance, the same program, by just replacing the training sets, could be used to recognize other types of cars Therefore, the input should be an arbitrary image, and the output should be that same image with a rectangle around any occurrence of the car we are searching for (see Fig 117) The system will work by directly searching for an occurrence of the positive in the image, while treating all subwindows of the image the same way.

It will not rst search for a general vehicle class and then specify the model of the vehicle This is a different and much more complicated task that is not easily solvable by machine learning Any occurrence of a rectangle around a part of the image that is not a rear of a Honda Accord 2004 is considered a negative detection The image size in the testing set is arbitrary, while the image sizes in both the negative and positive training sets are the same Positive training examples are the rears of Hondas The data set was collected by taking pictures of Hondas (about.

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Speci es a song to the ROI to be played later Each song is associated with a song number which the Play command uses to select the song to play Users can specify up to 16 songs with up to 16 notes per song Each note is speci ed by a note number using MIDI note de nitions and a duration speci ed in fractions of a second The number of data bytes varies depending on the length of the song speci ed A one note song is speci ed by four data bytes For each additional note, two data bytes must be added The ROI must be in passive, safe, or full mode to accept this command This command does not change the mode Serial sequence: [140] [Song Number] [Song Length] [Note Number 1] [Note Duration 1] [Note Number 2] [Note Duration 2] etc.

This is a shift in mind set for the learning-system designer: instead of trying to design a learning algorithm that is accurate over the entire space, we can instead focus on nding weak learning algorithms that only need to be better than random On the other hand, some caveats are certainly in order The actual performance of boosting on a particular problem is clearly dependent on the data and the weak learner Consistent with theory, boosting can fail to perform well given insuf cient data, overly complex weak hypotheses, or weak hypotheses that are too weak Boosting seems to be especially susceptible to noise Schapire and Singer [23] described several improvements to Freund and Schapire s [8] original AdaBoost algorithm, particularly in a setting in which hypotheses may assign con dences to each of their predictions.

One who actively induces infringement of a patent is liable as an infringer One who sells a component of a patented invention, knowing the component to be a nonstandard item especially adapted for use in an infringement of the patent, is liable as a contributory infringer The chief limitation on the doctrine of equivalents is file wrapper estoppel, which prevents recapturing, through a broad interpretation of claim language, scope that was surrendered during prosecution of the patent application The recent decision in the Festo case has retroactively eliminated all equivalents of any claim limitation that was amended during prosecution Patent litigation involves the risks that the patent(s)-in-suit will be found to be invalid, unenforceable, or not infringed Moreover, it is possible that a prevailing patentee will recover less than the costs of the litigation.

Suit may be brought only in a court that has jurisdiction over both the subject matter of the litigation (subject matter litigation) and the person of the defendant (personal jurisdiction) Personal jurisdiction may be based on the defendant s place of residence or, in some circumstances, on the defendant s actions (long-arm jurisdiction) In addition, suit may be brought only in the proper venue, which is that court, or courts, among all those having jurisdiction, where the rules provide that the case may be heard Choice of venue may have a significant impact on the cost and outcome of litigation Generally speaking, plaintiffs in patent infringement lawsuits should demand a jury, rather than a bench trial, as they thereby have a greater chance of prevailing A party that has standing may bring an action, called a declaratory judgment (DJ.

) action, seeking to have a patent held invalid, unenforceable, or not infringed Standing is essentially a reasonable belief of an imminent suit for infringement by a patentee Such a belief may arise out of communications from the patentee to a possible infringer..

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