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Some critical values for the manufacture of cooked ham and shoulder, and of heat-processed sausages and p t s are given in Tables 31.5 and 31.6, respectively. Most products are lled into casings and molds, heated to core temperatures between 68 and 80 C, and subsequently handled and sliced. Hence, their shelf life is limited to a few weeks under refrigeration, and they are usually spoiled by recontaminant bacteria. For products with extended shelf life, even at ambient temperatures, additional factors are important. For cooked dried sausages, these include formulation (water, fat, salt) and water loss during

4 Press the Choose button, next to classi er 5 Select Classi ers Bayes Naive Bayes from the navigation hierarchy 6 Click OK to close the WrapperSubsetEval dialog The evaluation method for AttributeSelection is now speci ed 7 On the AttributeSelection dialog, press the Choose button next to search 8 Select AttributeSelection GeneticSearch from the navigation hierarchy 9 Press OK to close the AttributeSelection dialog The evaluator and search methods for attribute selection have been speci ed; however, our changes haven t yet been applied to our data set 10 Press the Apply button on the right side of the Explorer panel After processing the command, WEKA displays updated results in the Explorer panel In particular, under Attributes, notice the list now shows seven predictor attributes That is, the two attributes single-cell-size and mitoses have been removed from the attribute list.

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Let s reclassify the records using naive Bayes with 10-fold cross-validation; however, this time only seven attributes are input to the classi er 1 Select the Classify Tab 2 Under Classi er, press the Choose button 3 Select Classi ers Bayes Naive Bayes from the navigation hierarchy 4 Cross-validation is speci ed 5 Click Start Now, naive Bayes reports 9678% (661/683) classi cation accuracy, which indicates that the second model outperforms the rst model by almost 005% (9678% versus 9634%) That is, classi cation accuracy has increased where only seven of the nine attributes are speci ed as input Although these results do not show a dramatic improvement in accuracy, this simple example has demonstrated how WEKA s Genetic Search algorithm can be included as part of an attribute selection approach.

The thumb is drawn as a small circle inside the trackbar channel. The color of the thumb depends on whether or not the thumb is pressed. After the thumb is drawn, OnDrawTrackbar (and hence the NM_CUSTOMDRAW notification) returns CDRF_SKIPDEFAULT. Since the application has customized the appearance of the thumb, it needs to prevent the control from drawing the default thumb. The channel is drawn as a white rectangle of dimensions determined at the beginning of the function. This also returns CDRF_SKIPDEFAULT. The most interesting case is the TBCD_TICS case. We wanted the trackbar to have a rounded border with a thin black outline. OnDrawTrackbar draws this outline here because trackbar tic marks are drawn before the thumb and channel. Since drawing the rounded outlines is done with calls to the Windows CE function RoundRect, anything inside the specified rectangle dimensions will be drawn over. Therefore, drawing the outline during the tic mark pre-paint notification doesn t erase the custom thumb or channel. Notice how the application returns CDRF_DODEFAULT after the TBCD_TICS draw processing. The application only drew the control outline here, not the tic marks. To force the control to draw the standard tic marks, CDRF_DODEFAULT is returned.

The Bluetooth transceiver is operating in the 2.4-GHz ISM band. This section brie y explains the requirements for a Bluetooth transceiver operating in this unlicensed band. 5.6.1 Frequency Bands and Channel Arrangement

Let s further examine the results reported by WEKA s Genetic Search method, where the characteristics of the candidate population are described The following procedures should look similar to those performed above This time, however, we re invoking the attribute selection lter from WEKA s Select attributes Tab, which provides detailed output 1 Return to the Preprocess Tab on the Explorer panel 2 Press the Undo button (top right) This removes the lter we applied to the data set earlier 3 Select the Select attributes Tab from the Explorer panel 4 Under Attribute Evaluator, press the Choose button 5 Select AttributeSelection WrapperSubsetEval from the navigation hierarchy 6 Click on the text WrapperSubsetEval to open the WrapperSubsetEval dialog..

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